CCG SVL Exchange

This submission contains an implementation of:

Ruth Brenk, Alessandro Schipani, Daniel James, Agata Krasowski, Ian Hugh Gilbert, Julie Frearson, and Paul Graham Wyatt
Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
ChemMedChem 2008, 3, 435 – 444
DOI: 10.1002/cmdc.200700139
Installation:

1. Copy q_brenk.svl to $HOME/moefiles/svl, where $HOME is the users home directory
2. Restart MOE to load the new file

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

Usage:

1. In the Database Viewer select the Compute | Descriptors menu. The new descriptors will be added to the list. If you type BRENK in the "Filter" box at the bottom of the panel, then just these descriptors will be shown. Select the descriptor that you want to calculate and click the OK button.
2. To annotate a database of compounds with the rules that match compounds:
   svl> db_Brenk ['in.mdb', 'mol']
3. The Brenk Annotation will also be added to the ligand properties in the main window

KNIME:

Copy the included node_brenk_splitter.svl and node_brenk_annotate.svl in your custom node directory eg. $MOE/custom/knime and restart KNIME. The BRENK Splitter node can be used to filter compounds out of a data table. The BRENK Annotation node will give you flags for the rule hit by the molecules (first hitted rule only!).
Remarks:
The SMARTS from the supporting information of the paper have been translated to MOE SMARTS and checked against a large dataset. If you find any problem with this implementation please contact CCG Support.

The Brain Exposure Efficiency (BEE) Score

Mayuri Gupta, Thomas Bogdanowicz, Mark A. Reed, Christopher J. Barden, and Donald F. Weaver
ACS Chemical Neuroscience 2020 11 (2), 205-224
DOI: 10.1021/acschemneuro.9b00650

Description:

Calculates the following descriptors based on the reference above:

CODE	DESCRIPTION	CLASS
tr_pgp	PGP Transporter	i3D
tr_bcrp	BCRP Transporter	i3D
tr_oct1	OCT1 Transporter	i3D
tr_oct2	OCT2 Transporter	i3D
tr_pgp_sub	PGP Transporter Substrate	i3D
tr_bcrp_sub	BCRP Transporter Substrate	i3D
tr_oct1_sub	OCT1 Transporter Substrate	i3D
tr_oct2_sub	OCT2 Transporter Substrate	i3D
bee_Kpuu_micro	BEE Score Kpuu microdialysis	i3D
bee_Kpuu_homo	BEE Score Kpuu homogenate	i3D
bee_Kpuu_brain1	BEE Score Kpuu brain slice	i3D
bee_Kpuu_brain2	BEE Score Kpuu brain slice (Friden dataset)	i3D
bee_C_ub_std	Bee Score [C u,b]std	i3D'

Installation:

1. Copy this file to $HOME/moefiles/svl, where $HOME is the user's home directory (e.g., Windows: C:\Users\ernest\moefiles\svl or Linux/Unix/macOS: ~/moefiles/svl/)
2. Restart MOE to load the new file

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

Usage:

Go to (DBV | Compute | Descriptors | Calculate...) to open the Calculate Descriptors panel. Use the Filter to find the descriptors, select them, then press OK.

The VM2 application from VeraChem (https://www.verachem.com/products/vm2/) calculates protein-ligand binding affinities using mining-minima free energy methods. This SVL package contains an application to import the VM2 output directories into a single MOE database for convenient analysis and an application to interactively analyze the results in MOE database. The instructions for installation and usage are provided in a PDF in the zip-file.

This SVL is meant as an example showings how to work with popup windows, flat graphic objects and the WTK in SVL. As everybody should be familiar with the game I won't give a detailed description here. If you need information you can find them in the www. To use this implementation:
Run this file in MOE. Click on the circle buttons in the active row to select available colors from the popup. Click OK button when your guess is complete.

View PIEDA is an add-on program to visualizes PIEDA in FMODB.
PIEDA(Pair Interaction Energy Decomposition Analysis) [1] is to analysis by decomposing the Inter Fragment Interaction Energy (IFIE) obtained by the Fragment Molecular Orbital (FMO) method
into the
Electrostatic Interaction Energy (ES),
exchange repulsion energy (EX),
charge-transfer and mixing term (CT+mix),
and dispersion energy (DI).

The PIEDA components have correspondences such as ionic bond: ES term, steric hindrance: EX term, hydrogen bond: ES+CT+mix term, and CH-pi, pi-pi interaction: DI term.
The PIEDA file (*.out) can be downloaded from FMODB (https://drugdesign.riken.jp/FMODB/)[2].

Please see pdf for installation directions and a tutorial.


[1] Fedorov, Dmitri G., and Kazuo Kitaura.
"Pair interaction energy decomposition analysis."
Journal of computational chemistry 28.1 (2007): 222-237.

[2] Watanabe, Chiduru, et al.
"Development of an automated fragment molecular orbital (FMO) calculation protocol toward construction of quantum mechanical calculation database for large biomolecules."
Chem-Bio Informatics Journal 19 (2019): 5-18.

Description:
This package contains the zipped Update Site for the MOE Extensions for KNIME Version 2.4.8, the documentation and an installation video.
It is meant for installation on computers that cannot access the KNIME Partner Extension Update Site.
Before installation make sure the KNIME Base Chemistry Types&Nodes Extension is installed.
It has been tested with KNIME 4.6.1.


Installation:

• Start KNIME
• Click on Help|Install New Software.
• Click the "Add" button.
• Click on "archive" and browse to the zip file from this download.
• Click OK and follow the instructions on the screen.
• Restart KNIME (MOE category in the Node Repository will be shown)
• Set the path to MOE in the KNIME Perferences (KNIME|File|Preferences|Chemical Computing Group)

More nodes are available as separate download from the SVL Exchange.

Example Workflows are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

Description:
This package contains more than 200 MOE nodes for KNIME written in SVL and the documentation.
To use the nodes the MOE Extensions for KNIME have to be installed in KNIME either from the KNIME Partner Extension Update Site or from the MOE Extensions for KNIME (Offline Installation) available for download from the SVL Exchange.

Installation:

• Extract the zip archive from this download into $MOE/custom.
• Make sure the path to $MOE is set in the KNIME Preferences (KNIME|File|Preferences|Chemical Computing Group)
• Restart KNIME

These nodes have been tested with MOE 2022.

Example Workflows are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

Description:
This package contains the documentation for the MOE Extensions for KNIME as pdf file. It also includes three videos showing the different steps for on-, offline installation and activation of SVL based nodes.

The MOE Extensions for KNIME and the SVL based nodes are NOT included and have to be downloaded separately.

The workflow examples from the tutorial are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

Downloading this submission will create a new directory on your desktop. In this directory you will find a folder with files for each exercise in the documentation.

Calculates Central Nervous System Multiparameter Optimized Index from

"Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties."
Wager TT, Hou X, Verhoest PR, Villalobos A.
ACS Chem Neurosci. 2010 Jun 16;1(6):435-49. doi: 10.1021/cn100008c. Epub 2010 Mar 25.

Calculates CNS BBB Score from "The Blood-Brain Barrier (BBB) Score"
Gupta, M, Lee, H J, Barden, C J, and Weaver, D F.,
J. Med. Chem., DOI: 10.1021/acs.jmedchem.9b01220 (2019)

1. Save this file in moefiles/svl
2. Use DBV | Compute | Descriptors | Calculate

This runfile allows you to do a shape similarity search of a molecule in MOE or a database of molecules/poses against another database.

This tool has been designed to bring together a variety of clustering methods which share a common framework.
Here is an overview of what this tool can do:

• Fingerprint, protein sequence and conformational clustering
• Jarvis-Patrick clustering
• Parallelized K-Means clustering for large datasets
• Hierarchical clustering (Single-, complete- and average-linkage)
• Parameter optimization methods (Silhouette score, Dunn index, Purity, Rand index)
• Cluster assignment of an independent dataset with a pre-existing fingerprint cluster model
• Clustering of subsets (selected, random, first N, time window, mseq or descriptor-based criteria)

This download contains only the manual. Please see page 2 for package download and installation directions.

Installation and Usage

1. Extract the zip-archive in $HOME/moefiles
2. Open a database in DBV and click DBV|Compute|Molecule|Graphics.
3. Select the plots you would like toi add (some require the receptor in MOE).

Introduction:
The Bio-MOE package contains a series of custom SVL applications that are used for biologics modeling. These applications, accessible from the Sequence Editor and the Database Viewer menus, include tools for antibody humanization and back mutation prioritization, liability detection, patch visualization, and PTM motif annotation, aid importing and exporting collections of sequences, as well as visualizing protein properties and coloring antibody motifs.

This download contains only the manual.

Installation:

• Download and installation instructions are page 3 of the document

Usage

1. Load AlphaFold-predicted structure in MOE
   *You can get example structures from AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/)
2. Open the svl file from the Open panel to startup a 'AF-Coloring' panel.
   *You can also startup the panel by executing 'af_coloring []' in SVL Commands window after loading the svl file.
3. In 'AF-Coloring' panel, click 'Apply' button

This script is used to import certain entries from a database containing docked poses into another database containing selected docked poses. The user must specify the database containing the docked poses they wish to import (mdb), a database to import poses into (pose_mdb), and a vector of entry numbers that correspond to the docked poses they wish to import (entry_numbers). If pose_mdb does not already exist, it will be created prior to entry import.

Directions:Run the command 揼et_selected_poses [mdb, pose_mdb, entry_numbers]?after saving and loading the SVL script.

REINVENT [1] is a SMILES generative model based on the Recurrent Neural Network implemented in the programming language Python. The models supervised-learned by the compound library can be further adjusted by reinforcement learning that incorporates scoring functions such as fingerprint similarity and activity prediction models. MOE's REINVENT interface provides the following features along with an easy-to-use GUI.

• Scoring functions of MOE: MOE descriptor, QSAR model, fingerprint model, pharmacophore, consensus model, and docking score can be used as scoring functions in reinforcement learning.
• Executing and monitoring of reinforcement learning: Various parameters can be set from the GUI to execute reinforcement learning. While running, you can monitor the structure of the generated SMILES and its score.
• Scoring samled compounds: SMILES sampled by the generative model can be converted to 3D and scoring functions can be applied.
• GUI for creating Prior Network and supervised learning: Not only SMILES files but also MDB and SDF can be used as input data to create Prior Network and supervised learning from the GUI.

For more detail, Please see the attached PDF manual.
[1] Olivecrona, Marcus, et al. "Molecular de-novo design through deep reinforcement learning." Journal of cheminformatics 9.1 (2017): 1-14.

Installation:
Place menu-extra-mmgbvi in $MOE/custom/menu and mmgbvi.svl in $MOE/custom/svl/run where $MOE is your main MOE installation directory, and creating directories where necessary.

Database AutoPH4 Installation

The db_AutoPH4.zip folder contains the following:

1. A PDF file of containing the description of the application.
2. The custom folder containing
   a /menu subfolder with the menu items for the AutoPH4 in the main GUI (MOE | Compute | Pharmacophore | AutoPH4) and Database AutoPH4 in the database viewer (DBV | Compute | Pharmacophore | AutoPH4).
   b. /sample folder with files for examples in this document.
   c. /svl subfolder with
   i. AutoPH4 Application SVL code /svl/AutoPH4.svl
   ii. Database AutoPH4 /svl/db_AutoPH4.svl
   

Installation Instructions

1. Copy all the subdirectories in the SVL folder (i.e. menu, sample and svl) to your $MOE/custom, folder, where $MOE is your MOE installation directory.
2. Restart MOE. The new code and menu items will be loaded.
3. Once installed, the standard AutoPH4 panel can be launched from the MOE window
   (MOE| Compute | Pharmacophore | AutoPH4).
   Database AutoPH4 is launched from a database viewer
   (DBV | Compute | Pharmacophore | AutoPH4).

In 2013 the group of Prof. Rarey (Uni Hamburg) in cooperation with the CADD group of Roche AG published the Torsion Analyzer.
This program accesses the quality (i.e. the strain) of individual bonds of the conformation of a molecule based on statistics in structural databases.
The bonds are colored in green/yellow/red based on their individual quality.

INSTALL INSTRUCTIONS:

• $HOME needs to be replaced with your home folder (e.g. C:/users/myaccount/)
• Folders that do not already exist need to be created.

1. Make sure to use MOE 2018.0101 or newer. Check the Window Title of the MOE window for the MOE version number.
2. Get a copy of the XML file that contains the relevant data from the website of either the Uni Hamburg (1a) or the CCDC (1b).
3. Get the XML (Version 2.1) from the website of the Rarey workgroup:
   https://www.zbh.uni-hamburg.de/forschung/amd/datasets.html
4. Get the XML (TorsionAnalyzer CSD library) that includes CSD-derived data from:
   https://www.ccdc.cam.ac.uk/support-and-resources/downloads/
   This will require a valid CSD license (to be bought from the CCDC)!
5. Rename the downloaded XML file to rochetor.xml and copy it to $HOME/moefiles/lib/
6. Extract the attached rochetor.zip and copy
   rochetor.svl to $HOME/moefiles/svl/
   rochetor.menu to $HOME/moefiles/menu/
   
7. Restart MOE. There will be a red button button "TorAnalyzer" in the RHS. This button will allow to toggle the Torsion Analyzer.

REFERENCES:

• Torsion Angle Preferences in Druglike Chemical Space: A Comprehensive Guide
  Schaerfer,C., Schulz-Gasch,T., Ehrlich,H.C., Guba,W., Rarey,M., Stahl,M.
  J. Med. Chem. 56 (2013) 2016-2028 http://dx.doi.org/10.1021/jm3016816


• Torsion Library Reloaded: A New Version of Expert-Derived SMARTS Rules for Assessing Conformations of Small Molecules.
  Guba, W., Meyder, A., Rarey, M., and Hert, J.
  J. Chem. Info. Model. (2015) DOI: acs.jcim.5b00522

The authors of the of the original Torsion Analyzer are not affiliated with the authors of this implementation! Please report all bugs to support@chemcomp.com!
*** update ***
Terminal rotors, such as terminal methy groups, are now filtered. WARNING: This will reduce the number of torsion angles reported by the QuaSAR descriptors!

This rotamer library contains the known naturally occuring RNA modifications published as well as some additional isomorphs. Chiral information were obtained from the MODOMICS database of modified residues.

Add nomr_rotamers.mdb to the Rotamer Library

1. Create a new directory in $HOME called nnnb. (If this directory already exists, use the existing one.)
2. Move nomr_rotamers.mdb to $HOME/nnnb
3. Open the Protein Defaults settings. (MOE | Settings | Protein Defaults...)
4. Add the new rotamer library MDB $HOME/nnnb/nomr_rotamers.mdb to the Rotamer Library field separating them using a semi-colon (";").
5. Click Apply.

Validate the new Rotamer Library files

To ensure that your new rotamer library files are properly registered and contain no errors, click on Validate.
The newly added rotamer library mdb ($HOME/nnnb/nomr_rotamers.mdb) should now be listed as a defined rotamer library file.

Use the new nucleic acids in MOE applications

The new nucleic acids should now appear in the DNA/RNA Builder | More... list.

This is an implementation of the well known connect 4 game. It was written as a christmas gimmick for 2021. SVL learners can use it as an example for creating a window panel programmatically and implementing a recursive function in SVL.

The tool in this package allows for the prediction of ternary complexes mediated by molecular glues. Conceptually similar to bifunctional degraders (often referred to as PROTACs), molecular glues tend to be smaller and more drug-like, as the two proteins in a ternary complex interact with the same single moiety in a molecular glue. As a consequence of this simplification, however, rationally predicting the behavior of a putative molecular glue has proven elusive. Thus, the tool in this package allows for additional experimental information to be optionally provided, to guide the predictions and thereby generate more accurate results. A demonstration of this tool in both a limited knowledge scenario (suitable for initial exploration of ternary complex structure) and an extensive knowledge scenario (where a known, effective glue design is being modified) can be viewed at https://drive.google.com/file/d/1_NlB7QJkPTRxcX6N6hnp65imrItiUu8w/view. Please note: This download contains only the instruction manual. To download the scripts and other necessary files, please email mdrummond@chemcomp.com

REQUIREMENTS

A MOE database of packed molecule conformations (in mol:conf field). NOTE: Conformations must be pre-generated with relevant settings.

USAGE

GUI:

Drag-and-drop this file onto the MOE window to run it.
-OR-
Navigate to the file on your machine using the Open panel. Then press OK to run it.
Both options will lead to a GUI prompt to select or open the database to run the function on.

Commandline:

1. Copy this file to $HOME/moefiles/svl/run/, where $HOME is the user’s home directory (e.g., Windows: C:\Users\walter\moefiles\svl\run\ or Linux/Unix/macOS: ~/moefiles/svl/run/)
Notes:

• The directory must be created if it does not already exist.
• Alternatively, drag-and-drop this SVL file in MOE window to run.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME
  

2. In an SVL function, in a MOE menu file or at the SVL command line, type a command like
run 'db_smid.svl'

This function is used to split a database into a specified number of subset databases, ideally for use prior to conformational searches or small molecule docking. If the number of entries in the database to be split is not evenly divisible by the number of databases, all subset databases will contain an equal number of entries except the last (which will contain the equal number plus the remainder of entries).
Directions:
Run the command db_subset_generation [mdb, num_dbs] after saving and loading the SVL script.

The RF Interactions tool identifies, scores and visualizes receptor-ligand interactions based on their statistical siognificance. From these interactions, the tool calculates per-ligand-atom scores. The methodology is described in detail in this publication:
Augmenting structure-based design with experimental protein-ligand interaction data: molecular recognition, interactive visualization and rescoring.
A. Tosstorff, J.C. Cole, R. Bartelt, B. Kuhn
Chem. Med. Chem. (2021)
https://doi.org/10.1002/cmdc.202100387

Note, that this submission only contains the ligand atom types described in the publication! Most ligand atoms will remain untyped using the included CSVs. Thus, this submission mostly acts as a proof of concept and documentation for the referenced paper. New atom types and associated statistics can be added by the user.
For complete lookup tables, please contact Bernd Kuhn (bernd.kuhn@roche.com)

INSTALL INSTRUCTIONS:
$HOME needs to be replaced with your home folder (e.g. C:/users/myaccount/) Folders that do not already exist need to be created.

• copy the .svl file to $HOME/moefiles/svl/
• copy the .menu file to $HOME/moefiles/menu/
• copy all .csv files to a folder of your choice
• restart MOE
• set the CWD to the folder where the CSV files are located (this is only required to be done once)
• a new menu entry MOE | Extra | RF Interactions will be added
• click on the menu entry
• clicking on it for the first time will read the CSV files and cache their content for future use

This SVL code allows to create a sequence consensus logo.
It implements the approach published in:
T.D.Schneider and R.M.Stephens
(1990) Nucl.Acids Res. 18:6097-100
doi: 10.1093/nar/18.20.6097
Installation:

1. Copy the file to $HOME/moefiles/svl/run, where $HOME is your home directory

Usage:

1. In the sequence editor select the chains to create a consensus logo for.
2. Open an SVL command line and type: run 'consensus_logo.svl'
3. In the Consensus Logo panel press Apply.
4. To generate an image of the logo press the button Export.

To install extract the zip file into your MOE installation folder. After loading the file you can enter a Mcule ID in the text widget of the builder (e.g. MCULE-9380369173) to get the structure or use the Interface MOE|Extra|Mcule to search or import structures from Mcule. A Authorization Token is required for searches. the token can be entered in the settings (gear wheel button). The availability will be displayed in the Ligand Properties. If you have questions, please let us know.

This script allows a user to generate .PNG images of every ligand-receptor complex contained in a database, ideally to maintain a sense of continuity when generating images of multiple docked poses for use in publications. Users are able to customize various aspects of visualization such as point-of-view, receptor ribbon type/color, and ligand atom color. Optionally, users can also display specific residues based on UID number and customize residue atom color. Visualization of each docked pose contained in a database will be written to a numbered 600 DPI .PNG file named after the database.
Directions: Run the command docking_figuregen_mdb [mdb, ribbon_type, ligand_color, rec_color, res_color, res_nums] after loading the script. When the first ligand-receptor complex is brought into the system, the user is prompted to select their desired point-of-view and visualization settings (background color, ribbon quality, etc.) and confirm their settings by clicking 'OK' in the MOE ribbon. Colors for the 'ligand_color', 'rec_color', and 'res_color' arguments are given by the PLOT_COLORS function. Both the 'res_color' and 'res_nums' arguments are optional and should be used only if a user desires to display sidechain atoms of specific receptor residues.

Installation:

1. Put 'node_psilo_search_if.svl' in $MOE/custom/knime or the directory which is specified by ' SVL Node Directories:' of ' KNIME Preferences '.
2. Restart KNIME.
3. The 'PSILO Interface' node will be found in 'MOE | IO | Input' of the 'Node Repository' tree.

Notes:

• The PSILO server must be defined in the configuration of the MOE installation.

Installation:

1. Copy the titledview.svl into $HOME/moefiles/svl/run
2. Copy tiledview.menu into $HOME/moefiles/menu
3. Restart MOE.
4. Use the button in the button bar to start the tool.

Installation:

1. Copy rsc_lookup.svl to $HOME/moefiles/svl, where $HOME is the user's home directory
2. Copy menu_sysman_rsc and moe-menus-settings-rsc under $HOME/moefiles/menu
3. Restart MOE to load the new files
4. Follow the instructions under the following link and create an API key:
5. https://developer.rsc.org/get-started
6. Please note that the free account allows 1000 API calls per month.
7. Use MOE | Settings | RSC Login to enter the API key and press Apply, Close.

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

USAGE:

Right click on the System Manager ligand line to see the new menu item "RSC Lookup"

Installation:

1. Copy ebi_lookup.svl to $HOME/moefiles/svl, where $HOME is the user's home directory
2. Copy menu_sysman_ebi under $HOME/moefiles/menu
3. Restart MOE to load the new files

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

USAGE:

Right click on the System Manager ligand line to see the new menu item EBI Lookup For a database of molecules, use ebilookup_mdb ['test.mdb','mol'] to lookup mol field

Description:
This package contains more than 200 MOE nodes for KNIME written in SVL and the documentation.
To use the nodes the MOE Extensions for KNIME have to be installed in KNIME either from the KNIME Partner Extension Update Site or from the MOE Extensions for KNIME (Offline Installation) available for download from the SVL Exchange.

Installation:

• Extract the zip archive from this download into $MOE/custom.
• Make sure the path to $MOE is set in the KNIME Preferences (KNIME|File|Preferences|Chemical Computing Group)
• Restart KNIME

These nodes have been tested with MOE 2020.

Example Workflows are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

Use reverse fingerprinting to highlight active motifs, color atoms by activity contribution and develop pharmacophore queries.

Description:
This update replaces the use of the RCSB's legacy RESTful API in MOE 2019.0102 by the new RCSB Data and Search APIs.
The legacy APIs will be unavailable between Nov 3-10, 2020, and permanently discontinued on Dec 9, 2020.

Instructions:

1. cd $MOE
2. tar zxvf rcsbapi_2019.0102_patch.tgz

Installation:

1. Copy moe_scenes.svl to $HOME/moefiles/svl, where $HOME is the user's home directory
2. Copy menu_extra_moe_scenes under $HOME/moefiles/menu
3. Copy the sample file moe_scenes_sample.mdb under a desired folder, for example c:\temp or $MOE/sample/mol
4. Restart MOE to load the new file

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

USAGE:

Use MOE | Extra | MOE Scenes to start the application.
Browse to see the sample database from step 3 above.

Here is an overview of what this tool can do:

• Processes either a single structure in MOE or on a series of structures in a MOE database
• Generates protein patches and a translucent vdw surface over the protein
• Generates protein descriptors at various pHs
• Allows one to browse through a database of structures with the protein patches shown along with the corresponding 2D patch map
• Creates protein patch report(s) in either ASCII or XML format

The titration algorithm uses Henderson-Hasselbalch kinetics and PropKa to estimate average amino acid charges at a given pH and
local 3D environment that would be normally be present in an ensemble. This is done using a single static structure.
This tool may be called from the command line for batch processing.
Please see pdf for installation directions and a description of the application

Here is an overview of what this tool can do:

• Generates a series of protein conformations with control over the amount of movement in the backbone atoms and CDR loops (if present)
• For each conformer, a series of pre-defined protein patch, hydrophobic index and hydrophobic interaction chromatography (HIC) descriptors are computed and averaged

Options exist to save the individual conformations and their associated descriptors.
Note that the conformations generated are minimized and are not subjected to Protonate 3D as the sampling option in the current Protein Properties application does.
This tool may be called from the command line for batch processing.
Please see pdf for installation directions and a description of the application.

Use this script to multiply a previously loaded and pre-oriented molecule and to distribute it randomly rotated onto a 2D plane.
You can define the number of lipids along the X and Z side of the plane and the spacing in between for the X and Z coordinates. The distance does not take the shape of the lipid in account, it is the distance from the center of one lipid to the next one.
A green arrow is displayed in the MOE window. The molecule must be oriented so that the head of the arrow is close to the lipid headgroup (with a high Y value such as +10) and the tails have a low Y value (eg -10).
You can do this by selecting the atoms and rotating them by holding the Alt key and dragging the middle mouse button.
This can also be done using the MembraneEditor from
http://Cm2.CELLmicrocosmos.org

*HOWTO prepare the molecule with the MembraneEditor*
Start the MembraneEditor (e.g. via Webstart from the website). Import your molecule as a lipid into the Local Database of the MembraneEditor. Add the molecule to the membrane. Drag and Drop the lipid from the Overview to the membrane in the Membrane View. Goto the PDB Output Settings window. Change the settings for the Origin of Coordinates for the XZ plane and Y axis to the physical center. Now just export the project to a PDB file. Load the molecule now into MOE and start this script via the SVL Text Editor.
This script is based on the algorithm in
Krueger J, Fischer WB.
Exploring the conformational space of Vpu from HIV-1: a versatile adaptable protein.
J Comput Chem. 2008 Nov 15;29(14):2416-24.

For the evaluation in conjunction with the CELLmicrocosmos MembraneEditor (and about how to do membrane packing in a better way), please refer to:
Sommer B, Dingersen T, Gamroth C, Schneider SE, Rubert S, Krueger J, Dietz KJ
CELLmicrocosmos 2.2 MembraneEditor: A Modular Interactive Shape-Based Software Approach To Solve Heterogeneous Membrane Packing Problems.
J Chem Inf Model. 2011 Apr 19.;51(5):1165-82.

USAGE

1. Copy this file to $HOME/moefiles/svl/run/, where $HOME is the user's home directory (e.g., Windows: C:\Users\walter\moefiles\svl\run\ or Linux/Unix/macOS: ~/moefiles/svl/run/)
   Notes:
   
   • The directory must be created if it does not already exist.
   • Alternatively, drag-and-drop this SVL file in MOE window to run.
   • $HOME can be determined at the SVL Commands windows. Type:
     svl> HOME
     
2. Load your lipid monomer into the MOE window. In an SVL function, in a MOE menu file or at the SVL command line, type a command like:
   run 'moe_membrane_grid.svl'
   A GUI panel opens. Adjust the lipid so that it is aligned along the axis shown in the green arrow.

BOILED-Egg model by Daina et. al (2016) for predicting gastrointestinal absorption and brain penetration was adapted for MOEsaic. The y-axis (WlogP) of the model was substituted for the SlogP descriptor. The Model was re-optimized according to the dataset's SlogP vs TPSA.

A Monte Carlo (MC) method was used for computing the model, similar to the methodology described by Daina et al. To obtain optimal ellipses, 10⁹ independent MC runs were performed. True and false positives (TP, FP) as well as true and false negatives (FP, FN) were used for the binning statistics of each ellipse and evaluated using Matthews correlation coefficient (MCC ranges from -1, total disagreement between observation and prediction, to 1, perfect agreement).

The model achieve an MCC of 0.682 and 0.791 for the human instestinal absorption (HIA) and blood-brain-barrier (BBB) datasets, respectively. The original model reported similar MCC (0.653 and 0.747 for the HIA and BBB datasets, respectively).

How to include the model in MOEsaic:
This adapted model is available via this SVL function provided in the package. Copy the SVL file to the custom/svl directory of the moe installation. Restart the MOE/web server. The model should now be available in any MOEsaic session. Version 2020.09 adds support for moesaic_ModelFit and MOE descriptors based on the "Boiled Egg" model.

Installation:

1. Copy projectdock.svl to $HOME/moefiles/svl, where $HOME is the user's home directory (e.g.,Windows: C:/Users/walter/moefiles/svl or Linux/Unix/macOS: ~/moefiles/svl/)
2. Copy menu_extra_projectdock to $HOME/moefiles/menu
3. Restart MOE to load the new file.

This submission contains a string aware version of the database calculator. It can be used to concatenate multiple char fields or to work on a single char fields.
The panel also allows to use numerical or molecular fields. If a molecule field is included the molecule name will used inside the formula. The return type can be char or numeric. For numeric return types the function atoi or atof will be used respectively. Field can be added to the formula by clicking on available fields on the right. For comments, requests and feedback, please contact support at chemcomp.com.

AutoQSAR is revised version of AutoQuaSAR. It allows you to easily perform operations related to QSAR modeling (molecular standardization, descriptor calculation, independent variable selection, cross validation, outlier exclusion).
QSAR-Evolution is revised version of QuaSAR-Evolution. It optimizes QSAR models by selecting independent variables based on genetic algorithm. AutoQSAR provides I/F of it.
Please see pdf for installation directions and a tutorial.

An automated method for generating pharmacophore models from protein binding pockets

This script will allow a user to calculate feature to feature/binding pocket centroid to feature distances for every feature present in a pharmacophore query file. This script uses the SiteFinder function in an automated manner to elucidate probable binding site residues. Once residue selection is completed, a dummy atom is placed at the mean position of all atoms comprising the SiteFinder selected residues and distances between pharmacophore features and from each pharmacophore feature to binding pocket centroid (represented by the placed dummy atom) are calculated. After distance calculation, matrices of feature to feature/binding pocket centroid to feature distances (as well as the maximum, minimum, and average values of these matrices) are printed to the SVL commands window.

Directions: Run the command <>ph4_distcalc [ph4, receptor] after loading the script.

Installation:

1. Extract the package named MOEWeb_QuickDock to a temporay folder.
   Follow the instructions below to install the extracted files.
2. Extract the archives named dock.svl under $MOE/web on the MOEWeb server.
3. Download JSMOL viewer from the following link and extract under $MOE/web on the MOEWeb server. https://sourceforge.net/projects/jsmol/
4. Install the svl script from the 'svl' folder of the extracted files to the following location, both on MOEWeb server, and your MOE desktop client. If folders shown below do not exist, please create them.

   Filename	Location
   dock_ui.svl	$HOME/moefiles/svl/run
   moeweb_quickdock_util.svl	$HOME/moefiles/svl
   soap_web_fileutil.svl	$HOME/moefiles/svl

5. Place the menu file menu_extra_moewebdockutil from the 'menu' folder of the extracted files under $HOME/moefiles/menu
6. Restart the MOEWeb server by explicitely loading the soap_web_fileutil.svl, for example by adding the following line to MOEWeb configuration file ($MOE/web/moewebservice.cfg on Windows or equivalent on Linux):
   -load /home/$USERNAME/moefiles/svl/soap_web_fileutil.svl
   
7. Browse to the main page of the MOEWeb to see the icon for the newly added application named QuickDock
   You may need to force a refresh of the MOEWeb page, the first time after upgrade. This is often done by pressing ctrl+F5 or holding ctrl and pressing the refresh button on the browser.

Usage:

1. Browse to the main page of the MOEWeb server, locate the icon for the newly added application 'QuickDock' and click on it to start the application.
2. Browse to locate a *.sdf file containing the ligand, or use the sketcher to sketch the input molecule. You may also paste the ligand in *.mol format into the skether.
3. Under the Docking Options choose one of the targets prepared by the MOEWeb administrator.
4. Press MOE-Dock to start the docking.
5. Once docking is finished, use the links from the results page to download individual files.

Adding new targets:

1. Start desktop MOE which has the extensions from Step 2 of the installation.
2. Open the target structure from any source and prepare it as desired.
3. Open the Docking application via MOE | Compute | Dock .
4. Configure the docking as desired.
5. Once ready to run, press the 'Batch...' button and save the batch file.
   Ensure to use a descriptive name.
   This file includes the structure of the target, cocrystallized ligand (for comparison), and all the required settings.
6. Copy this file to the MOEWeb server under $MOE/web/dock/scripts.
7. Alternatively, open the panel via MOE | Extra | MOEWeb Dock File Transfer and use it to upload the batch script to the MOEWeb server.
8. Browse to QuickDock application page. Refresh the page if needed by pressing ctrl+F5. The new target name must appear under the list.

Sketch ligands or load from *.PDB/*.SDF files to perform conformational search.

Installation:

1. Extract confsearch.zip under $MOE/web
2. Download JSMol viewer from the following link and extract it under $MOE/web:
   http://jsmol.sourceforge.net/
   
3. Restart MOEWeb. A new icon titled Conformational Search appears under MOEWeb starting page.

Description:
This package will predict how molecules in MOE (or in a MOE Database) might be metabolized by Cytochrome P450s, aka CYPs. Specifically, the following properties are evaluated:

• Selectivity -- Using two different QSAR methods, predict which of the five main human isoforms of CYP (1A2, 2C9, 2C19, 2D6, and 3A4) might interact with a provided ligand
• Reactivity -- Using two different QSAR methods, predict whether a provided ligand might act as a Substrate or an Inhibitor for one of the five main human CYP isoforms
• Sites of Metabolism -- Using a pharmacophoric approach, predict which atom(s) on a provided ligand might be metabolized by one of three reactions -- Aliphatic Oxidation, Aromatic Oxidation, and N-Dealkylation -- catalyzed by the two most prevalent CYP isoforms -- 2D6 and 3A4.

To install, copy cyp_predictor.svl and the CYP_Files directory to $HOME/moefiles/svl. To launch CYP Predictor, double-click on cyp_predictor.svl from the MOE File | Open panel.

Description:
The tool performs principal component analysis of atomic coordinates on trajectory data obtained by molecular dynamics and show the motion of molecules along each pricipal component axis in MOE window. Please see pdf for installation directions and a tutorial.

This submission is an implementation of:
J.Dunbar, A.Fuchs, J.Shi, C.M.Deane (2013) PEDS 26(10):611-20
https://doi.org/10.1093/protein/gzt020

Installation:

1. Copy abangle.svl to $HOME/moefiles/svl/, where $HOME is your home directory
2. Restart MOE to load the file

Usage:

1. The SVL code implements 6 descriptors that match the ABangle descriptors: ABangle_dc, ABangle_HL, ABangle_HC1, ABangle_HC2, ABangle_LC1, ABangle_LC2.
   See the Dunbar paper for definition of these descriptors.
2. The SVL code also implements a graphical mode that renders the vectors. This only works if exactly one Fv is present in MOE.
   Call this SVL function:
   ABangle [Chains [], [annotate: 1, render: 1, render_color: 0x00ffff]]

This submission contains an implementation of:

Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.
J Med Chem. 2010 Apr 8;53(7):2719-40.
PubMed PMID: 20131845.
http://dx.doi.org/10.1021/jm901137j

Installation:

1. Copy q_pains.svl to $HOME/moefiles/svl, where $HOME is the users home directory
2. Restart MOE to load the new file

Notes:

• The directory must be created if it does not already exist.
• $HOME can be determined at the SVL Commands windows. Type:
  svl> HOME

Usage:

1. In the Database Viewer select the Compute | Descriptors menu. The new descriptors will be added to the list. If you type PAINS in the "Filter" box at the bottom of the panel, then just these descriptors will be shown. Select the descriptor that you want to calculate and click the OK button.
2. To annotate a database of compounds with the rules that match compounds:
   svl> db_PAINS ['in.mdb', 'mol']
3. The PAINS will also be added to the ligand properties in the main window

KNIME:

Copy the included node_pains_splitter.svl and node_pains_annotate.svl in your custom node directory eg. $MOE/custom/knime and restart KNIME. The PAINS Splitter node can be used to filter PAINS out of a data table. The PAINS Annotation node will give you flags for the rule hit by the molecules (firts hitted rule only!).
Remarks:
The rules were defined as SLN in the original publication. For the usage in MOE they have been translated into SMARTS and manually checked. Due to different aromaticity perception slight differences are expected. If you find any problem with this implementation please contact CCG Support.

Peptide and peptide-containing polymer structures are often stored in SMILES strings or other formats which do not retain information about amino-acid monomer identities and their relative positions in the polymeric structure. The "split_into_peptide_residues.svl" application attempts to regenerate the monomeric residues and their polymeric arrangement given a molecular structure where this information is absent. The amino acids are detected and named using the currently loaded rotamer library in MOE, and additional optional libraries of non-amino acid functionalities can also be provided for detection and naming of residue units.

This script will detect and encode the sequence information.

Last Update: February 2020

This script is meant to automate the generation of multiple batch docking jobs where a single ligand is being docked into a database of proteins. This script outputs multiple .svl files that can then be docked on another computer. This script requires user input during the generation of the batch files but greatly automates the process.

If you run this SVL in MOE, it will ask you to select a MOE-Grid file to create a molecular surface. You can adjust the surface isolevel with a slider. Then you can use another grid file for the coloring of the surface. A typical application is creating a surface based on an electron density grid and using an electrostatic potential grid for the coloring.

Locating 'hot-spot' for binding to a protein structure is the goal of many computational chemistry applications.
Hot-spot analysis can help validate the importance of known interactions in holo complexes and suggest regions to target when designing ligands for apo structures. In the context of formulating biological-based medicines such as antibodies, hot-spots can be used to determine locations where excipients and other formulation molecules may aggregate on the protein surface and affect bulk properties of the medicine.

Open the SVL file in the MOE text editor then save and load the script from the text editor window.
Enter this command:
ligandRMSD ['reference structure', 'database', 'mol', 'receptor']
where "reference structure" is a .pdb or .moe file containing a crystallized reference structure with ligand bound and "database" is a .mdb file containing the results of a MOE docking run.
Ligand RMSD values will be written to a new column in the docking results database.

Description:
The five Methods in this PROTAC modeling package are designed to enable the in silico screening, comparison, and potentially the design of PROTAC molecules. Specifically, these tools have been designed to tackle two fundamental challenges in PROTAC modeling:

1. The prediction of the conformational ensemble accessible for a constructed ternary complex. The makeup of this conformational ensemble depends not only on the conformational flexibility of the PROTAC (primarily imparted via its linker), but also on direct protein-protein interactions. Conceptually, this challenge is similar to pose generation in traditional small molecule docking.
2. The identification of ternary complex poses generated by the conformational sampling above that are deemed biologically relevant, i.e., that resemble known ternary complexes. Conceptually, this challenge is similar to pose scoring in traditional small molecule docking.

A third fundamental challenge — the fruitful interaction of each binding end of the PROTAC with its respective protein — is readily addressable in MOE using standard Structure-Based Drug Design techniques. This challenge is not addressed by these tools, i.e., it is assumed that the binder moieties are active against their respective targets.
Please note: This download contains only the instruction manual. To download the scripts and other necessary files, please email mdrummond@chemcomp.com

Description:
This script contains fragmentation and visualization GUI for ABINIT-MP Fragment Molecular Orbital method program in MOE.

Installation:

• Extract zip archive into your favorite directory ($FMOE_DIR)
• write the code running loader.svl to $HOME/moefiles/start.svl (local) or $MOE/custom/start.svl (global)

local function main []
 run '$FMOE_DIR/loader.svl';
endfunction

Source code repository:
https://github.com/drugdesign/FMOe

Description:
This script accepts HDX data from HDExaminer and DynamX software and maps it onto protein structures into MOE. Multiple protein chains can be colored with different HDX input files, so that changes in the HDX signal during complexation can be visualized for multimeric proteins. An automated alignment procedure ensures that differences in the numbering schemes between the HDX data and the residue numbers within MOE can be handled. Information for the individual peptides in included, as is the ability to color by difference or in side-by-side mode. Finally, the poses in a protein-protein docking database can be colored and scored by how well they correlate with the experimental HDX results.

Description:
This package contains more than 150 MOE nodes for KNIME written in SVL and the documentation.
To use the nodes the MOE Extensions for KNIME have to be installed in KNIME either from the KNIME Partner Extension Update Site or from the MOE Extensions for KNIME (Offline Installation) available for download from the SVL Exchange.

Installation:

• Extract the zip archive from this download into $MOE/custom.
• Make sure the path to $MOE is set in the KNIME Preferences (KNIME|File|Preferences|Chemical Computing Group)
• Restart KNIME

These nodes have been tested with MOE 2019 but are also backward compatible to MOE 2018.

Example Workflows are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

Description:
This package contains more than 150 MOE nodes for KNIME written in SVL and the documentation.
To use the nodes the MOE Extensions for KNIME have to be installed in KNIME either from the KNIME Partner Extension Update Site or from the MOE Extensions for KNIME (Offline Installation) available for download from the SVL Exchange.

Installation:

• Extract the zip archive from this download into $MOE/custom.
• Make sure the path to $MOE is set in the KNIME Preferences (KNIME|File|Preferences|Chemical Computing Group)
• Restart KNIME

These nodes have been tested with MOE 2018 but are also backward compatible to MOE 2016.

Example Workflows are available from the KNIME Hub.

For any questions contact support@chemcomp.com.

QFSS, Quick Federated Structure Search, provides means to search huge database stored across multiple mdb files. Based on our original substructure fingerprint and indexing technology, you can handle huge molecular database containing even hundreds of millions of entries stored separately in multiple mdb files. QFSS does not require any database engine, such as Oracle or SQL Server, nor high spec hardware. Even a laptop PC, you can search tens of millions entries in your mdb files.

QFSS SOAP version is also available. Via SOAP functions, you can run QFSS on remote server from your MOE client or from internet browser.

QFSS package contains SVL code, SSFP definition file and sample databases(ChEMBL and Leadlike Conformer Database) with index files for your convenience. The attached file contains only the manual. To receive the full download instructions for this package please contact ccg@molsis.co.jp

This script enables SVL highlighting in a katepart texteditor such as KWrite/Kate/KDevelop (part of KDE).
SVL files are recognized using their file extension .svl.

Kate is available in your repos (Linux) or here: https://kate-editor.org/ (Linux, Windows, Mac).


Installation (version numbers might need to be adapted to your KDE/katepart version):

Linux:
Copy the extracted xml file to

/usr/share/kde4/apps/katepart/syntax (all users installation; needs root privileges)

or

$HOME/.kde4/share/apps/katepart/syntax (single user installation)


Windows:
$HOME\AppData\Local\katepart5\syntax (single user installation)


*** Update ***
function list: MOE2020.09

Warning: This version does NOT work on moe-type database fields.

These protein descriptors are calculated on sequence only. I.e. there is no need for atomic data.

INSTRUCTIONS:
- copy this file to $HOME/moefiles/svl/
($HOME is your home folder)
- restart MOE
- the new descriptors will be available from:
DBV | Compute | Descriptors | Calculate...

List of descriptors:
seq_nChains: Number of chains, regardless of type.
seq_nChainsAA: Number of chains that contain at least one amino acid residue. (protein or peptidic ligand)
seq_nRes: Total number of residues.
seq_nResAA: Total number of amino acid residues.
seq_n***: Overall number of amino acid residues of type ***. *** = amino acid 3-letter code.
seq_MW: Sequence based protein mass in kilodalton. I.e. sum of internal amino acids masses + free termini. The masses correspond to the mass of the most abundant protomeric and isomeric species at pH = 7.0. I.e. Glu,Asp are deprotonated, Lys,Arg are protonated, His and all other amino acids are neutral. One pair of free termini is added per amino acid chain.
seq_n+: Number of positively charged amino acids. I.e. seq_n+ = seq_nLys + seq_nArg
seq_n-: Number of negatively charged amino acids. I.e. seq_n- = seq_nAsp + seq_nGlu
seq_PI: Sequence based isoelectric point as calculated in MOE release code ($MOE/svl/biopolymer/proprop.svl).
seq_aliphIdx: Aliphatic index. Ref: A. Ikai (1980) J Biochem 88: 1895-8
seq_GRAVY: Grand Average of Hydrophathy score. Ref: J. Kyte, R.F. Doolittle (1982) J Mol Biol 157: 105-32
seq_polariz: Sum of residue polarizabilities based on M. Swart (2004) J Comp Meth Sci Eng 4: 419-25. Please note that polarizabity varies with conformation!
seq_InstabIdx: Instability Index. Ref: K. Guruprasad, B.V.B. Reddy, M.W. Pandit (1990) Prot Eng 4: 155-61
seq_PI_Bellqvist: Sequence based isoelectric point as published by B. Bjellqvist et al. (1993) Electrophoresis 14: 1023-31
seq_ExtCoeff_280: Extinction Coefficient at 280 nm assuming that half of the cysteins form cystines.
seq_Absorb_280: Absorbance at 280 nm (0.1%).

--------------------------------------------------------------------
Description:
--------------------------------------------------------------------
Code to compute the PBSA estimate of the solvation free energy
of selected atoms in the MOE window (or on a database of molecules)

--------------------------------------------------------------------
Usage:
--------------------------------------------------------------------
MOE:
(1) Load the structure of interest
(2) Run this code. A GUI will appear.
(3) Set the 'Source:' option to MOE (atoms in the MOE window)
(4) Use the option pull-down to specify the atoms whose solvation
energy you wish to measure
(5) Press 'Compute'. The PBSA solvation energy will be written the
text field in the panel. e.g,

PB Solvation Energy: -8.00656 (kcal/mol)

MDB:
(1) Run this code. A GUI will appear
(2) Set the 'Source:' to MDB
(3) Use the 'VBrowse..' button to select the database
(3) Press 'Compute'. The panel will disappear and the PBSA solvation
energy will be computed for all entries in the database and
written to the field 'PBSA_sol'


TITRATE (i.e.., loop over salts and conditions)
(1) Load this function
(2) Enter

svl>PBSA_Solvation_Energy_MDB_titrate opts

at the SVL command line. Here opts is a tagged vector of options:

opts= [
dbfile: 'database filename',
mfield:'molecule field'
(defaults to first mol field in database)
salts: vector of salt tokens
(default: SALTS_TO_USE = ['NaCl','KCl','MgCl2'];
concs: vector of concentrations
(default: [0.1, 0.05, 0.01];)
temps: vector of temps
(default: 300)
]
(3) The code will loop over all combinations of salts, conc.
and temperature and output the PBSA solvation energies to
the database. The conditions are recorded in the database field
names
--------------------------------------------------------------------

The Excel interface is based on the Apache POI library which has to be downloaded and installed separately. We also recommend having Oracle Java installed.

Installation:
1) Download the Apache POI Library from https://poi.apache.org
2) Extract the downloaded archive file to your hard drive.
3) Add the poi library to your CLASSPATH environment variable.
for example if you extracted the library in c:/javalibs/poi-4.0.1
you would have to add:
C:/javalibs/poi-4.0.1/*;c:/javalibs/poi-4.0.1/ooxml-lib/*;c:/javalibs/poi-4.0.1/lib/*
to CLASSPATH.

4) Extract the archive file you downloaded from the SVL Exchange website.

5) Copy io_excel.svl into $MOE/custom/svl or $HOME/moefiles/svl

6) Copy SVL_Excel.class into $MOE/java

7) In MOE|File|Open you should get the option to Import the XLS file when selecting an excel file.

8) To export an excel file use:
db_ExportXLS[
'$MOE/sample/mol/gmp_inhibitors.mdb',
'$HOME/gmp_inhibitors.xlsx',
'Sheetname',
0,
0
];
where the first number after the sheetname is a flag indicating that only selected entries should be exported.
The second number is for selected fields only.

sdconf and sdconf.bat are tools that can generate small molecule conformations from the operating system command line. They can be combined with other SD files, as explained in the document "SD Pipelining Tools - How to Create a Fragment Database" that can be accessed from (MOE | Help | Tutorials)

Download and unpack the zip file. This contains the SVL script, a PDF document and example files.

(1) Select the protein_pocket_volumes.svl file from a MOE| File | Open panel.

(2) A GUI will appear.

(3) Use the 'Pocket Set A' and 'Pocket Set B' options to select the 'A set' and the 'B set' for the operation. The pocket will be defined around the ligand atoms. To compare proteins with no ligands, first use the Site Finder to create dummy atoms in the binding site. These will then be used to identify the pockets.

(4) Press Create. The pocket volume comparison will proceed and a new graphics object rendering the volume difference will be create and listed in the panel. The volume enclosed by each surface is shown in name of the graphic object.

To install extract the zip file into your MOE installation folder. After restarting MOE you will find a new entry in the configuration (gear wheel) menu (MolPort). Enter the username and password you got from MolPort there. Click OK. Now you can enter a MolPort ID in the text widget of the builder (e.g. MolPort-000-219-650) to get the structure or use the Interface MOE|Extra|MolPort to search or import structures from MolPort. Databases for imported structures contain links to the MolPort website which can be opened from the database browser by clicking (Open in Browser).
If you have questions, please let us know.

Based on a given alignment of a set of small molecules with activities, a grid potential analysis is applied using the principles of CoMFA. Hereby, steric and electrostatic interaction energies of a positively charged carbon probe atom placed on grid points around the aligned molecules are determined and grid potentials are calculated. Then a partial least square model is created by fitting the activities to the grid point potential values. This process starts with a principal component analysis of the descriptors to reduce the number of components in the model to a value set by the user. The model coefficients for the principal components are then mapped back to the descriptors. These contributions can be visualized to identify areas with positive or negative contribution to the model spatially (pre-condition: numerically higher activity values mean higher biological activities).

This script provides a few basic tests to measure the performance of hardware/software running MOE.

Extract the archive to $HOME/moefiles (for current user) or $MOE/custom (for all users).

DESCRIPTION:
This package contains more than 100 small molecule descriptors.
Most of these molecular descriptors are suitable for statistical modelling of molecular properties or activities (e.g. machine learning).

All descriptors use the descriptor plug-in system in MOE. The are divided 3 classes:
2D - conformation-independent
i3D - conformation-dependent
x3D - dependent on conformation and the structure currently in your MOE window

Note: each of the descriptor files contains a number of related descriptors. There are redundant descriptors in case they make sense in more than one file.



INSTALLATION:
1) unzip attached folder
2) Copy the desired descriptor file(s) to $HOME/moefiles/svl, where $HOME is your home directory
(e.g., Windows: C:\Users\walter\moefiles\svl\ or Unix: ~/moefiles/svl/)
3) Restart MOE to load the new file

Notes:
- The directory must be created if it does not already exist.
- $HOME can be determined at the SVL Commands windows. Type:
svl> HOME


USAGE:
The new descriptors are available from the Calculate Descriptors panel
(DBV | Compute | Descriptors | Calculate).

Rogers et al., (ECFP)
J. Chem. Inf. Model. 2010, 50, 742-154;

Differences to the Pipeline Pilot implementation may arise from:
- differences in treating charge-seperation of some moieties (Nitro etc.)
- hypervalent atom treatment (e.g. Sulphur and Phosphorous oxides)
- a different aromaticity perception

update 10-Oct-2018 (ma) E/FCFP now available as MOE descriptors

!!!
NOTE: This latest version (last change: 04-feb-2016 (ma))of the script generates fingerprints that are incompatible to earlier versions
!!!

This is a simple XYZ parser which uses the plug-in system of the MOE Open and Save panels.

Please note that there are multiple XYZ format descriptions. The one used here is:

5

C 0.0000 0.0000 0.0000
H 1.0900 0.0000 0.0000
H -0.3633 0.7267 0.7267
H -0.3633 -0.9926 0.2660
H -0.3633 0.2660 -0.9926

This is mostly an example of how to use the plug-in systems in MOE.

INSTALLATION:
- Copy the file to $HOME/moefiles/svl/ ($HOME is your home folder).
- Restart MOE.
- The XYZ file format should now be available in the Open and Save panel.

Simply unzip the contents to your sublime folder Packages/User/. After restarting Sublime 3 you will have at the bottom right a new language from the drop-down list:

Scientific Vector Language

Sublime should also be able to autodetect SVL files by their extension .svl.

Automatic flexible alignment for all molecules in a database against reference
molecules from the main MOE window. This function uses the FlexAlign []
function. If an output database filename is not supplied then it
will be based on the input filename. For instance if the input database
was in.mdb, the output database would be in_align_all.mdb.

If you set the molecule source to "MOE" then it runs as in the normal
Flexible Alignment panel in MOE.

USAGE

Save this file to your hard disk. For it to be available for use by all
of the users of MOE, create a directory called $MOE/lib/svl/custom/run
and save this file to that directory. eg on Windows and Linux,
this might be: c:\moe\lib\svl\custom\run\align_all.svl
/usr/local/moe/lib/svl/custom/run/align_all.svl

For the script to be available for use by a single user, then it
is best to create a directory in your home directory called svl,
and create a directory called run in that directory. Save this
file into that directory. eg on Windows and Linux, this might be:

c:\Documents and Settings\user_name\svl\run\align_all.svl
/usr/people/user_name/svl/run/align_all.svl

(a) From the MOE | File | Open panel, select this file
and click the "Run" button.

(b) Alternatively, at the SVL command line, type:
run 'align_all.svl'

The underlying SVL function is align_all. To use this, "load" this
file and at the SVL command prompt, type a command like:

svl> align_all []
svl> align_all ['in.mdb', 'out.mdb', options]
svl> align_all ['in.mdb', ', [heavy_rms:0.1, nfail:40]] //refinment only
svl> align_all ['in.mdb', 'out.mdb', [file:']]

- If you use MOE batch, then you have to pass the name of the input MDB file or
the script will fail. eg
moebatch -exec "run['align_all.svl', ['in.mdb',',[nfail:10]]]"

Align_all options are the following:

esel: 0, // use selected entries only
nsolns: 0, // no. of output solutions
mfield: ', // molecule field
dump: 1, // suppress CLI output if 0
fixed: 1, // fix template coordinates
write_template: 1 // 1: write only aligned pose to output (Pose)
// 2: write both in one field (Alignment)
// 3: write pose and template to diff fields (both)

To run the script with the "refine only" option, pass an option with file:'.
For other flexible alignment options see FLEX_DEFAULTS below.


*References*
An Alternative Method for the Alignment of Molecular
Structures: Maximizing Electrostatic and Steric Overlap
Tetrahedron Computer Methodology, Vol. 3, No. 6C, pp615-633, 1990

A New Approach to Probing Conformational Space with
Molecular Mechanics: Random Incremental Pulse Search
J. Am. Chem. Soc. 1989, 111, 4371-4378
D.M.Ferguson D.J.Raber

Calculates CNS MPO from "Moving beyond Rules:
The Development of a Central Nervous System Multiparameter Optimization
(CNS MPO) Approach To Enable Alignment of Druglike Properties;
Wager TT, Hou X, Verhoest PR, Villalobos A.
ACS Chem Neurosci. 2010 Jun 16; 1(6): 435-449"

1. Save this file in $HOME/moefiles/svl
2. Use DBV | Compute | Descriptors | Calculate

The ZINC database is an excellent resource for structural information on small molecules. The script let's you import pre-calculated 3D conformations including relative energies from the DB2 format to a MOE Database. ZINC 2D structures can be downloaded as SDF file and imported using standard MOE tools (please contact support@chemcomp.com if you need further assistance).

Bivalent ligands are increasingly important such as for targeting G protein-coupled receptor (GPCR) dimers or proteolysis targeting chimeras (PROTACs).

This script builds models for the linker between two ligands, bound in two binding sites.

There is more information in the Open Access paper:

Pérez-Benito, Laura, et al. "The Size Matters? A Computational Tool to Design Bivalent Ligands." Bioinformatics.
https://doi.org/10.1093/bioinformatics/bty422

Additional information is in the supplementary information for the pap

Usage:
(1) Load this function
(2) At the SVL command line, enter

svl> db_moledit []

(3) Select target MDB file at the panel.

For more information, please see the bubblehelps.
This program may be useful if you want to extract ligand, receptor, or contacting solvent from molecule type field in MDB of molecular dynamics output.

Run PSILO searches from MOE

New in this version:
- Pocket Similarity Search
- Highlight hit atoms and meters for 3D interaction search
- Bugfixes

The following searches are currently implemented:
- Full Text Search
- Ligand Search (via SMILES /MOL)
- Small Molecule Name
- Protein Sequence Search
- Protein Name
- Title (PDB)
- Project Code
- PSILO Family
- Similar Pockets
- CIF Field
- 3D Search

Description:
Application to detect duplicate conformations in a MOE database.
Use cabn set RMSD theshold for unique conformations. Option of
eithe selecting of deleting duplicate conformations

Note: ALL entries must not be empty!

Usage:
(A) GUI
If you run this code from a MOE File Open panel(click on "Run SVL"),
a GUI will appear. Use the Browse button to select the input
database. Specify the molecule and (optional) mseq fields
(in the case of databases with multiple conformations).
Molecular symmetry will be taken into account.

Choose the actions for the duplicates - either select in the
database viewer or delete. Press OK to perform teh operation.

(B) Command Line:
The operation can be run using the SVL command:

svl> conf_unique opts

where opts is a tagged vector of options:

opts = [
dbfile:'database_filename',
mfield:'molecule field name'
mseq:'mseq field'
RMSD:'rmsd threshold in A'
action:'Select' or 'Delete' -> action to perform on duplicate

Installation Instructions Updated for MOE 2016

Please refer to the provided manual for details of usage.

To stay up to date about future updates, please subscribe to our newsletter: http://eepurl.com/_PqlD

How to cite Docktite:
Scholz, C.; Knorr, S.; Hamacher, K.; Schmidt, B., DOCKTITE — A Highly Versatile Step-by-Step Workflow for Covalent Docking and Virtual Screening in the Molecular Operating Environment. J. Chem. Inf. Model. 2015, 55, 398-406

This is a workflow for creating QSAR models in MOE. The process works like so:

1. Load (and optionally back up) an MDB of molecules with an activity field
2. Calculate descriptors and specify the name of the activity field
3. Split the database (optional step) into training and test sets. Various methods are available, including k means clustering and selection from binned activity values.
4. Select descriptors. The selection can be pruned by looking for low variance or highly correlated descriptors. In addition the training and test sets can be compared in PC space based on the current descriptor selection.
5. Build the QSAR model. Currently only the PLS and PCR methods are available.
6. Validate the model; a variety of methods, including MOE's standard Leave One Out method, are available.
7. If a test set has been created, the model can now be applied to the test set.

In the zip file you download you will find a PDF explaining how to use the Wizard and the SVL file itself. To start a QSARWizard session, simply save the SVL to your hard-drive and run it e.g. using the MOE | File | Open panel, browse to qsarwizard.svl and click OK.

RECON Descriptors

RECON is an algorithm for the rapid reconstruction of molecular charge
densities and charge density-based electronic properties of molecules,
using atomic charge density fragments precomputed from ab initio
wavefunctions. The method is based on Bader's quantum theory of Atoms
in Molecules.

A library of atomic charge density fragments has been built in a form
that allows for the rapid retrieval of the fragments and molecular
assembly. The RECON algorithm reads in a molecular database, determines
atom types and environments, assigns the closest match from the library
of atom types and combines the densities of the atomic fragments to
compute a large set of new and traditional QSAR descriptors. QSPR and
QSER indices for large pharmaceutical databases or proteins can be
computed within seconds.

This is an SVL implementation of MOE RECON TRAD. A much faster linux
executable with additional features is available by license through the
RPI office of Technology Commercialization http://www.rpitechnology.com

Relevant references:

S. Oloff, S. Zhang, N. Sukumar, C.M. Breneman*, and A. Tropsha*,
"Chemometric Analysis of Ligand Receptor Complementarity: Identifying
Complementary Ligands based on Receptor Information (CoLiBRI)",
Journal of Chemical Information and Modeling, 46 (2), 844-851, 2006.

B.K. Lavine, C.E. Davidson, W. Katt, C.M. Breneman*, "Analysis of
odor structure relationships using electronic Van der Waals surface
property descriptors and genetic algorithms," ACS Symposium Series
(2005), 894 Chemometrics and Cheminformatics, 127-143.

B.K. Lavine, C.E. Davidson, C.M. Breneman*, W. Katt, "Electronic van
der Waals surface property descriptors and genetic algorithms for
developing structure-activity correlations in olfactory databases"
Journal of Chemical Information and Computer Sciences, 43 (6):
1890-1905, Nov-Dec 2003.

C.M. Breneman* and M. Rhem, "A QSPR Analysis of HPLC Column Capacity
Factors for a set of High-Energy Materials Using Electronic Van der
Waals Surface Property Descriptors Computed by the Transferable Atom
Equivalent Method." J. Comp. Chem. 18(2),182-197, 1997.

http://www.drugmining.com/files/RECON/recondoc/WinRecon.html

Installation of RECON TAE Molecule Descriptors

Create the directory $MOE/custom/svl/ (if necessary)
Copy the q_recon_tae.svl file to $MOE/custom/svl/

For instance, if MOE is installed in c:\moe on a Windows PC, this would be
c:\moe\custom\svl\q_recon_tae.svl

If MOE is installed in /usr/local/lib/moe on a Unix system, this would be
/usr/local/lib/moe/custom/svl/q_recon_tae.svl

Copy entire 'reconBin' directory to a nice location (e.g. $MOE)

Set system variable 'recon_moe_root' to the reconBin directory (remember
to include the final '\' or '/' in your variable!) e.g.

recon_moe_root = c:\moe\reconBin\


An alternative to setting a system environment variable is to
replace line 2295 in q_recon_tae.svl with with a line that
points to the reconBin directory. Examples of this could be

local RecRoot = 'c:/moe/reconBin/';
or
local RecRoot = '/usr/local/lib/moe/reconBin/';

Note that paths within MOE use / forward slashes, on both Windows and Unix.

This scripts sends protein sequences to PFAM website and pareses the resulting hits, colors various detected domains and creates MOE 'sets' to access the domains as detected.

This is a basic interface to the ORCA QM package written by F. Neese. It allows to read and write ORCA coordinates (.xyz) and facilitates the creation of input files for reaction coordinate scans and constrained calculations.

USAGE:
MOE | File | Open -> ORCA_coordinates.svl

If you want to start ORCA from the panel the following executables and paths need to be specified in the script:
ORCA_DIR, QUEUE_TMP_DIR, MPI_EXE_DIR, MPI_LIB_DIR

The profiles allow you to create a series of input files with identical header and footer. E.g. calculation of NMR shieldings for different compounds with identical settings.

Press [Show] at the very bottom of the panel to get the input file w/o starting it locally or on a queing system.

For ORCA options etc. please refer to the ORCA documentation.

The output of ORCA calculations can be read by the script (geometric data only!). Scan calculations are imported to an MDB file and can be visualized using MOPAC_rb_energyTable.svl (uploaded to SVL exchange as well).

I do NOT guarantee that this script will work with future releases of ORCA and MOE.
Tested with MOE2015/2016, ORCA3.0.3 in a Linux environment and a SUN grid engine derivative.

UPDATE for MOE2016

All scripts have been tested with MOE2016.08 in a Linux environment.
MOE built-in public domain MOPAC does NOT work due to missing support of mixed cartesian and internal coordinates.

This collection consists of 5 svl scripts:
- MOPAC_rb_createInput.svl
- - create a MOPAC input file for 1D and 2D calculations
- MOPAC_rb_interpretOutput.svl & MOPAC_rb_lib.svl
- - extract structures and energies from MOPAC output file, import them into an .mdb
- MOPAC_rb_energyTable.svl
- - use the extracted structures and energies to visualize an "energy hypersurface"
- MOPAC_rb_applyFixed.svl
- - restore fix-flags when using a MOPAC output geometry as input for a new MOPAC calculation

These scripts are stored in a .zip file.


=== MOPAC_rb_createInput.svl ===
FEATURES:
- creates a MOPAC input file
- fixed atoms get no-optimization flag
- "normal" atoms will keep their cartesian form
- reaction coordinate atom(s) will be transformed into internal coordinates and appended to the "normal" atoms
- for restoration of atom-names and residues a .mmeta-file will be generated which can be used in

BEFORE running this script:
- adjust the constants MOPAC_EXE, MOPAC_LIC and MOPAC_LIB

USAGE:
- prepare the system you want to use as input (all atoms will be used - that is: every atom in Atoms [])
- run this svl-script via 'File' - 'Open'

=== MOPAC_rb_lib ===
- library functions for MOPAC_rb_interpretOutput.sv

=== MOPAC_rb_interpretOutput.svl ===
FEATURES:
- imports structures and energies of MOPAC output file
- if present, uses a .mmeta file to restore atom names and residues

BEFORE running this script:
- adjust the constant MOPAC_RB_LIB to point the location of the library file

USAGE:
- run this svl-script via 'File' - 'Open'


=== MOPAC_rb_energyTable.svl ===
FEATURES:
- visualizes an "energy hypersurface" of a MOPAC grid/scan calculation

USAGE:
- run this svl-script via 'File' - 'Open'
- select the .mdb file (needs to be same format as output of MOPAC_rb_interpretOutput.svl)


MOPAC_rb_applyFixed.svl
In MOE molecular databases (.mdb) are no information whether an atom is fixed or not (fix flag). For every no-optimization flagged atom present in the MOPAC input .dat-file this script looks for an atom of the same element at the same position in the MOE system, and fixes this atom.

FEATURES:
- restores the fix flags of an MOPAC output scene loaded from an .mdb

USAGE:
- load the MOPAC scene from the .mdb
- run this svl-script via 'File' - 'Open'
- select the .dat-file (MOPAC inputfile which has been used to generate the scene)
- PLEASE, check the results: 'Select' - 'Potential' - 'Fixed

INSTALLATION

1) Copy this file to $HOME/moefiles/svl/run/, where $HOME is the user’s home directory (e.g., Windows: C:\Users\walter\moefiles\svl\run\ or Linux/Unix/macOS: ~/moefiles/svl/run/)

2) Start the script using, from the SVL command line or a menu:
run 'plane_gui.svl'

Notes:
- The directory must be created if it does not already exist.
- Alternatively, drag-and-drop this SVL file in MOE window to run.
- $HOME can be determined at the SVL Commands windows. Type:
svl

Installation

1. Unpack the zip file in any directory.
2. Copy the two folders (menu and svl) to your $HOME/moefiles folder.
Now you should have $HOME/moefiles/svl/ted_extensions.svl and
$HOME/moefiles/menu/menu-ted_2016. Please be aware that the menu will over-ride
the default MOE text editor menu and any other modifications to those menu
you might have made.
3. Restart MOE.

Added Features

SVL Search (TED | Find | SVL Search or Ctrl + f in the TED window) - runs the SVL search utility. This allows a more fully featured search either in the current file or in MOE or User directories (as defined in step 4 of the installation process). Searches in file may fail if the file has been modified; please save then search if problems are encountered.

Manual Page (TED | SVL | Manual Page or Ctrl+m) - to enable this function, first type _UPDATE_ in a text editor window, highlight the text and choose TED | SVL | Manual Page. Once the index has been built (you can monitor the indexing in the SVL Commands Window), the manual page for an SVL function can be launched by highlighting the function name and choosing TED | SVL | Manual Page.

Default text blocks: Insert Header (Ctrl+h) and Database Looper Motif will insert a standard SVL file header (as seen in MOE SVL files) and a sample db_NextEntry-style database loop.

Block indent (Ctrl+. indents in, Ctrl+, indents out) - allows easy formatting of SVL. Select a few lines of text and indent it further right or left to make your code easier to read.

Block commenting (Ctrl+I comments out, Ctrl+Shift+I comments back in) -
allows easy commenting in/out of multiple lines of code in a similar manner to block indenting.

Move cursor back one word with Ctrl+b.

Unload the currently viewed SVL file from MOE by TED | SVL | Unload

TED | File | Save, TED | SVL | Save and Load, and TED | SVL | Save and Run all perform a "two stage" save, i.e. the last saved version is copied to a file with the same filename apart from an additional ~ suffix, then the main file is saved.

TED | Edit | Copy doesn't clear the clipboard if no text is selected

TED | Edit | Paste will delete any selected text before pasting at the cursor position.

TED | SVL | Save and Read Menu File will save the current file, reload the standard MOE menus then load the current file with the ReadMenuFile SVL command.

TED | SVL | Execute Command will execute any selected text, or if there is no selected text it will open a command line in the current text editor. This makes it fairly easy to step through a function executing one line at a time. Selected text is automatically expanded to complete lines. If the first line starts with "local", "global", "static", or "const" then that word is removed (this only affects the first line).

TED | SVL | Insert Date inserts today's date at the cursor position

TED | SVL | History Browser launches a listbox containing the contents of your history[]. Commands can be executed by selecting one or more of them and clicking "Execute", or can also be executed by double-clicking. Selected commands can be sent to a MOE text editor by clicking on "Make script".

This application uses a collection of proteins aligned using a common ligand and will highlight pharmacophore features that can be used to maximize
selectivity for each individual protein. An experimental scoring table (provided in the dabatase file imatrix.mdb) is used for this.

The resulting visuals can be used to modify the ligand to increase selectivity, i.e. to improve the activity on the chosen target while reducing the activity on others.

Sample files provided for quick testing.

This script will take as input the MDB from an MD simulation and write out every nth frame as a BMP file.
The user needs to set up the visualization before hand.

Load the protein in MOE and display ribbons. The "run" this script.
By default all the receptor chains with protein ribbons will be
modified. Chain keys can be supplied as an argument to adjust
only specific protein chains.

(a) From the MOE | File | Open panel, select this file
and click the "Run" button.

(b) Alternatively, at the SVL command line, type:
run 'ribbon_sidechains.svl'
run ['ribbon_sidechains.svl', protein_chains]

This implementation of the classic game tetris is published for demonstration purpose only and does NOT contain any scientific relevant methods etc.

For those of you, who need to relax some minutes ... enjoy

(Updated to work with file locking in MOE 2015.1001)

Syntax highlighting for the NEdit text editor for Motif/X11 available from https://sourceforge.net/projects/nedit/

To use this, download this file and save it to ~/.nedit/nedit.rc

Summary:
- Reads a MOE database or a folder containing MOE recognized structures
- Converts all files or database entries to the format give in 'Internal Format'
- Uses a simple mark-up syntax to generate command line required to call third party binary
- Uses the same format to designate the output files expected from third party binary
- collects the results in a MOE database.

It can also create batch scripts (unix only, for now) to do this offline.

The Scenes_Manager is a great way to enable collaboration between MOE users.
This will allow a user to save "scenes" in a fashion similar to other molecular viewers. Saved scences can be shared electronically via a .mse and accompanying .moe file.

It is common for small drug-like compounds and peptides that are constructed from SMILES strings and even SDfiles to have amide bonds where the hydrogen of the nitrogen atom and the oxygen of the carboxyl group result in a dihedral angle of approximately 0 degrees. This function sets all amide dihedral angles to 180 degrees and provides the option for energy minimization (this functionality is not implemented in the MDB version because of the energy minimization function with the MDB framework). The interactive (MOE 3D window) version allows the user to select the amide bond of interest.

The MDB version of this function provides the option to overwrite the original molecule or create a new column (field) for the compounds. The MDB version does indicate how many amide bonds were corrected. It is recommended that after changing the amide dihedral angle(s) within a database of compounds that the MDB energy minimization (MDB | Compute | Energy Minimize) function be performed using the desired molecular force field.

Note: Amide groups within rings are not modified.

The default values are:

No energy minimization for MOE 3D window (nrgOpt:0)
Column (field) with molecule is 'mol' (molField:'mol')
Save corrected compound to a new column 'modMol' (newMolField:'modMol')
Maximum peptide length is set with 'maxPeptideLength' (maxPeptideLength:15)

Instructions

Load this function
load '~/code/svl/utilities/db_SetAmide180.svl'

For a compound in the MOE 3D window (either a single small-organic drug-like compound or a selected molecule)
SetAmide180[]

For a compound in the MOE 3D window with energy minimization of the new conformation
SetAmide180[nrgOpt:1]

For a collection of compound in a MDB saving the new conformation to the originating column of the compound
db_SetAmide180['drugLike_compounds.mdb', [molField:'mol', newMolField:']]

This function counts the occurrence of the 166 MACCS keys for each molecule in a MOE database (mdb). The number of each key's occurrence is written to a user specified CSV file. The option to include the compounds' names and endpoint values in the CSV file is an option from the opts variable.

The default values are:

Column (field) with molecule is 'mol' (molField:'mol')
Include the endpoints if the endpoint column name is provided (endpointField:')
Include the compound's name or SMILES string. Not indicating a compound name file (nameField:') uses the compound's name returned with cName Chains[] or the compound's SMILES string if no name is present.
Column headers for MACCS keys will be returned in lower case letters (uppers:0)

Instructions

Load this function
load '~/code/svl/utilities/MACCSkeyCounts.svl';

Calculate the MACCS Key Counts for a MDB of compounds
MACCSkeyCounts['Selwood_dataset.mdb', 'Selwood_MACCSkeyCounts.csv', [molField:'mol', endpointField:'pEC50', nameField:'name']]

Display a MOE readable structure (*.moe, *.pdb, etc) via internet browser.

Requires jsmol. Please contact support@chemcomp.com if you have any difficulty installing jsmol.

residuesOfInterest = Residues[] |
cNumber rChain Residues[] == 2 or cNumber rChain Residues[] == 4;
atomEnvironment = Atoms[] |
cNumber aChain Atoms[] == 2 or cNumber aChain Atoms[] == 4 or
cNumber aChain Atoms[] == 5 or cNumber aChain Atoms[] == 6;
solvent_exposure_difference[
residuesOfInterest:residuesOfInterest,
atomEnvironment:atomEnvironment,
diffCutoff:0,
colorResidues:0,
printAtoms:1,
outfileSADiff:'solvent_exposure_difference.txt'];

DESCRIPTION:
The buttons contain: a molecular builder with added handling of chirality constraints, a simplified interface for flexible alignment of structures, a simplified interface for Contact Potentials and a pharmacophore query editor.

USAGE:
Save the attached files in your working directory (along with your molecule files) and open the moe-menus.pharmacophores file with the MOE File Open panel. A new set of buttons will appear at the left side of the MOE window.

D. Bondarev

Run Torsion Scan via internal MOPAC engine.


USAGE:

1. Load a desired small molecule structure in MOE.

2. Energy minimiza using MOPAC at AM1 level using
MOE | Compute | Energy Minimize | QM

3. Save this file to your working folder and run it by using
MOE | File | Open

then select 'torscan_mopac.svl' and press 'OK'

The code will run a '1scf' job via MOPAC and will display the zmatrix
created in MOPAC. Heavy atoms in main MOE window will be labelled
accordingly.

4. Select the desired dihedral from the list by clicking on the corresponding
line, then press 'Dihedral'.

ATTENTION: please verify that the selected atoms in main MOE window
correspond to a valid dihedral torsion. Currently we make no attempt
to verify this.


5. Press 'Run MOPAC'.

The script will use the internal MOPAC engine to run a torsion scan and
will plot the results.

The MOPAC input/output files can be displayed by checking 'Open Results
in Text Editor'.

These files are written into temp folder. To find the temp folder, Open
the SVL command window and type TMP. All files are named mopac*.*.

The current version uses external MOPAC such as MOPAC2012 for the actual
torsion scan, since some bugs in MOPAC7 affect certain structures.

Given a structure and a threshold in kcal/mol, the script first detects all "links". A link exists when two neighbouring residues have hydrophobic interaction stronger than the given thresholds.

The script then detects and isolates networks made solely via hydrophobic links as described. Users can include or exclude non-protein residues, multi chain interactions, etc.

An interactive histogram of all links in the system is also displayed, to help users set the hydrophobic threshold value.

This code computes an electrostatic similarity value between a molecule in the MOE window and (each of)a database of compounds previously aligned with the target molecule (for example, resulting from a FlexAlign procedure). It is observed that compounds with higher electrostatic similarity values may show similar biological activities. Thus this procedure may be used to rank docking poses where a known ligand is the target molecule, or can be used for ranking alternate scaffolds from a lead hopping program such as BREED. Electrostatic similarity is computed by solving the PB equation for both target and query compounds and computing a grid based Tanimoto (Tes) between the resulting grids. Thus results are in the range -1->1, with 1 = identical field and -1 = inverse field. This code uses the original PB solver in MOE prior to re-write in 2005.09 release and thus these dependent MOE functions are included in the script to work with versions of MOE released after 2004.

Usage :
(1) Load a molecule into the MOE window and compute MMFF charges. Also calculate MMFF charges for database ligands. nb all code has been tested using MMFF charges so these are recommended for correct use.
(2) Run db_ElectroSim[mdb,molfield] where the mdb contains a set of molecules in 'molfield' (defaults to mol if absent) to compute values for. Procedure outputs scores in field 'Tes' (range -1->1, where 1 = identical field)

Simply open the script if MOE: MOE | File | Open and you will get a graphical panel that allows you to pick the source, target, and number of entries per database.

Upon future execution, use the same output folder, it scans for already downloaded items and only downloads the new ones, and appends them to the last volume.

Syntax highlighting for the jEdit editor available from www.jedit.org

Save the xml file in JEDIT/modes and add the following lines at the end of JEDIT/modes/catalog

<MODE NAME="SVL" FILE="svl.xml"
FILE_NAME_GLOB="*.svl" /

Starting with a force-field energy minimized complex, the program calculates and displays the atomic interaction forces between the ligand and the pocket, as well as the per residue interaction energies of such interactions.

Originally published in Interaction force diagrams: new insight into ligand-receptor binding, H. Shadnia, J.S.Wright, J.M.Anderson Journal of Computer Aided Molecular Design, Vol. 23, No. 3, 1573,185-194, 2009.

Description
Use the EHT pharmacophore scheme to calculate where the Don2, Acc2, Aro and
Hyd features are, bin the distances along the triangle edges and convert to
fingerprint indexes. Hyd-Hyd-Hyd triangles are removed, and rings that are
both Hyd and Aro are only counted as Aro.

Usage
Load this file into MOE, e.g. save to $HOME/svl and restart MOE.
Use the fingerprint tools as normal, search for FP:EHTTAT.

DESCRIPTION:

db_nb_mols_incommon calculates a matrix for the number of compounds each database
has in common with each of the other databases based on either structure or
fingerprints.

db_compare_fp can be used to output the average tanimoto between two databases
based on fingerprints if called with

db_uniqBits_fp calculates a matrix for the number of unique fingerprint bits per
database, i.e.the number of bits not present in both compared databases

USAGE:

1. Save and load this function

2. A) At the SVL command line prompt type the following

svl> db_nb_mols_incommon [mdbs, outfile, opt]

where,

mdbs is a vector of tokens of filenames of the databases to
operate on
outfile is a token representing the filename of the output file
opt is a tagged vector of options
[
fp_flag: flag,
thresh: threshold,
fp_code: fingerprint code,
sim_code: similarity code,
average_Tani: flag,
write_cpds: flag,
cpds_outfile: cpds_outfile
]
- fp_flag: if 1 then similarity is calculated based on fingerprints
default is 0 (based on structure)
- molecules with similarity above the value of threshold are
regarded to be the same

- average_Tani: if 1 the the average similarity between two
databases is calculated instead of the number of molecules which
are regarded to be the same. fp_flag will be set to 1 automatically
(the average similarity is calculated in the following way: take the
maximum value for each row (so the highest similarity for each entry
to any other compound in the comparison database), then the average
value for those row-wise maxima.)

- fp_code and sim_code are the fingerprint and similarity metrics
to be used in the comparison. A list of fingperprints can be found
using the command ph4_FingerprintList []
Default options are fp_flag:0, thresh:0.85 using bit-packed MACCS
keys and the tanimoto coefficient.

- write_cpds: if 1 then another textfile is written containing all
compounds and the databases that contain the compound; if a cpd is
only found in one db, then it is not written to the textfile
- cpds_outfile: name of the output file for the above

2. B)
svl> db_uniqBits_fp [mdbs, outfile, opt]

mdbs is a vector of tokens of filenames of the databases to
operate on
outfile is a token representing the filename of the output file
opt is a tagged vector of options, in this case only the fp_code option,
e.g.: [fp_code:'FP:MACCS'] or [fp_code:'FP:piDAPH4']
A list of fingperprints can be found using the command
ph4_FingerprintList []

EXAMPLE:

db_nb_mols_incommon [['db1.mdb', 'db2.mdb', 'db3.mdb'], 'MatrixOut.txt']

// for all databases that are open in the database viewer
db_nb_mols_incommon [dbv_KeyList[], 'MatrixOut.txt']

// use fingerprint similarity
db_nb_mols_incommon [dbv_KeyList[], 'Matrix.txt',
[fp_code:'FP:MACCS', sim_code:'tanimoto', thresh:0.95, fp_flag:1]]

// output average tanimoto between two databases based on fingerprints
db_compare_fp[['a.mdb', 'b.mdb', 'c.mdb'], 'Matrix.txt',
[fp_code:'FP:MACCS', sim_code:'tanimoto', average_Tani:1]]

db_uniqBits_fp [['db1.mdb', 'db2.mdb', db3.mdb], 'Matrix.txt',
[fp_code:'FP:piDAPH4']]

A demonstration of MOE's url reading and XML parsing functions: given a list of RSS feed URLs (hard coded in the constant URL), read the XML and show the feed's contents as a set of buttons displaying title and first 140 characters of each feed's description. Clicking on the buttons should launch the relevant webpage in your browser.

I've included the SVL Exchange as the first feed; if clicking the button doesn't jump you straight to the item, first log in to the Exchange so the cookie is set in your browser then try again.

MOE can import .png format images, so I might be able to get those into the buttons one day.

Usage:

Run this file, e.g. use the File | Open panel, browse to rss.svl and click OK.

Align protein chains in MOE using external alignment tool MUSCLE (http://www.drive5.com/muscle)

DESCRIPTION:
MOGUL descriptors - get descriptors for the number of rare and unusual bond angles, lengths and torsions.


USAGE:
1. Save and load this function
2. Compute Descriptors:
DBV | Compute | Descriptors | Calculate
3. Search in the descriptor list for mogul

Simultaneously browse corresponding residues of aligned sequences. This utility uses MOE's selection language to define what to browse, what to hide, and what to keep displayed. It should be useful in comparing corresponding structures such as various crystal structures belonging to the same protein family.

Calculate and display twist angle between two planes, each defined via three atoms

Print mutation codes of a given protein chain vs a reference chain. Output in SVL command window and text file.

Export RISM grids from *.RISM files to *.CCP4 format so they can be displayed via MOE Grid Analyzer etc.

Constraint (staple) alignment of protein chains to current state

Builds Kinase Homology Models using input files for template and sequences.

Allows various fusion modes described in the accompanying PDF document.

Graphical panel to run sdfrag from MOE. This generates a database of fragments using six possible methods listed on the panel.

Renders the protein backbone as a graphical object

This script display the force-field potential status of the system in SVL text window and creates graphical text labels. It can be used to verify
whether atoms are fixed, inert, or tethered.

Creates restraints on alpha helix hydrogen bonds

Cycle transparency setting of all or selected atoms. Automatically assigns CTRL+F8 on keyboard to execute itself.

This is a script to connect molecular fragments from a database with a connection point of a scaffold.

For defining the connection point in
the database a SMARTS pattern is used, whereas the first atom in the SMARTS filter is the connection point.

The scaffold has to be loaded in MOE and its connection point atom has to be named "CON".

Only fragments with exact one connection point are written to the output database.
An mseq integer for every entry in the input database is required!

For use in MOE_Batch use a tagged vector. Tags are:
'smfilter' for the SMARTS Filter.
'dbinuput' for the input file.
'dboutput' for the output file.

Allows multi-processor utilization for LowModeMD and Stochastic conformational search.

Instructions:

For single user mode, place these files under $HOME/svl/run and restart MOE. If unsure about location of the $HOME folder, please run MOE and in the SVL command window type HOME and press enter. Please notice that the 'svl' and 'run folders may not exist under $HOME and should be created.

To install for all users, place under $MOE/lib/svl/custom/run and restart MOE.

For a single machine multi-processor mode use the command:
MOE -mpu N

Where N is the number of parallel processes. For best performance this should be set equal to the number of physical processors available. For example for a dual core machine use MOE -mpu 2 . Similarly for a Core i5 or Core i7 machine N should be 4 or 6 respectively. For network multi-processor mode please refer to MOE help pages under 'Installing and Running MOE/smp' .

Next, you can run the conformational search via
MOE | Compute | Conformations | Conformational Search ...

and choose LowModeMD or Stochastic search to run the search.

The script uses a GUI to manage the scenes. To start the GUI, type "Scenes_Gui []" in the command line or you can add a button to the menu.
Once you have a view of the atoms in the graphical window that you want to save, you can add it to the list of the saved ones by clicking "Append" or "Prepend". The program will ask you for a unique name and the you'll see the name in the list.
Clicking on the name will bring the scene back on the screen: visibility, colors, surfaces, ribbons and so on.
If the animation between the scenes is too slow, you can deactivate it or decrease the sleep time between frames in the Options.
Scenes can be deleted, moved, modified and renamed easily with the buttons in the GUI.

When saving the scenes (Save button in the Gui), the script saves two files: one with all the molecules in the system (.moe extension) and another one with the information about the visibility and color of the objects (.mse extension); this means that you can load the scenes only through the GUI by clicking on Load.
This means also that, if you want to share the scenes, you have to send both files.

DESCRIPTION:
Calculates a descriptor, Plane of Best Fit, after Firth et al., Plane of best Fit: A Novel Method to Characterize the Three-Dimensionality of Molecules, JCIM 2012, 52, 2516-2525.

USAGE:

1. Save and load this function, e.g. save to $HOME/svl and restart MOE
2. In the database view menu, choose DBV | Compute | Descriptors | Calculate
3. Look for a descriptor called PBF (type it into the Filter: line)

Description
An interface to allow substructure searching of a MOE database with the substructure defined by an external sketcher rather than SMARTS.

When this tool is run for the first time, the ten slots for stored searches will be empty. Click on New Sketch to launch the sketcher and create a substructure search; once transferred back to MOE, the sketch will appear in the first available slot. Click on the sketch search the selected MOE database for that substructure. If all ten slots are full, the first slot will be emptied to make room. Alternatively, Ctrl-click on a slot to remove that sketch from the history.

Usage
Run this SVL file, e.g. use the MOE | File | Open panel, browse to db_substructuresketch.svl and click OK.

The MOE Protein Properties application calculates a set of key physicochemical protein properties that are of interest in protein engineering applications.

If there is more than one receptor (protein) chain, a protein-protein interface interaction report is generated. For each pair of interacting residues there is a listing of the atom, residue and UID along with the type of interaction (HB, pi-pi etc), the distance and the strength.

To install the MOE/web application, unzip it into your $MOE/web folder and (re)start the MOE/web server. Alternatively unzip it to a different directory, e.g. webapps, then start the MOE/web server with the -app option:

$MOE/bin/moebeb -app webapps

The scripts finds all scaffold in a database, writes them to a separate database and calculates the complexity and cyclicity of the molecules like described in the paper. Also the frequency of the scaffold in the original database is written to the scaffold database.

Usage: load 'sca.svl' then use: db_SCAclassify['databasename',opt] where opt = 0 means that chiral constraints are not taken into account and 1 means they are taken into account. You may also run the script in GUI mode. In this case you will be prompted for a database and you can select how chiral constraints should be handled.

The MOE SA/Report tool uses a file ($MOE/web/sareport/units.txt) to determine both the units (eg. pIC50, IC50, percent) that a column in your data can have, and also regular expression style patterns in the column name that enable MOE to guess its type (eg. *PIC50 implies it is a PIC50). The format of the units.txt file is a little cryptic and difficult to edit by hand. Hence, this module allows the user to define new units or change the regular expression patterns, names or matching order of already defined units.

Place the module in $MOE/lib/svl/custom so it is loaded on startup, and run it without arguments from the SVL command line:
SAreport_range_editor [];

The GUI allows you to define, for each unit being edited, the following information:

* Autotype fields: a limited regular expression syntax consisting of asterisks at the beginning or end (or both) of a match string, and with multiple patterns separated by commas. Eg. *pK*,*pXC50*
* Name: a name for the new unit
* Type: activity (eligible for analysis by SAReport) or property (not eligible). Most new units should be defined as activities (even "properties" like MW or ClogP), unless you don't want to allow SA/Report to analyze them.
* Minimum, Mid-point and Maximum numeric points of the range for this data type. These will correspond to red, yellow and green colors respectively in the standard SA/REport three-color scheme, and values between these three thresholds will be interpolated.
* Multiplier/Offset/Function: scaling, addition and functional (eg. log) transforms applied while interpolating numbers between low/medium/high thresholds. I.e. y = f(ax+b)

PS: this module is referenced in the MOE UGM poster and ACS Philadelphia talk given by Deepak Bandyopadhyay of GSK in 2012.

When run, a menu is created in the Sequence Editor that allows the user to choose the set of residues to be acted upon and the renumbering action (whether it is on the alignment position or the residue ID) to be performed. This is useful for preparing a set of protein-ligand complexes for input into PLIF and Ligand Interactions, as those applications rely on a consistent rUID scheme across all receptors.

Additionally, antibody structures can be renumbered to a canonical scheme.

To use the code, run it, e.g. use the MOE | File | Open panel, browse to resIDfns.svl and click OK, or use this SVL command:

run 'renumber_residues.svl'

Classification of Organic Molecules by Molecular Quantum Numbers
Kong T. Nguyen, Lorenz C. Blum, Ruud van Deursen, and Jean-Louis Reymond
ChemMedChem (2009), 4:1803-1805
DOI: 10.1002/cmdc.200900317

USAGE:
1. Save and load this function
2. The 42 mqn_* Descriptors will be available in MOE

Run the file. A task called 'SurfSync' will show in the Cancel menu (and stay there). If you have a surface around some atoms and then move the atoms, the surface will move with them.

- If only a subset of defining atoms is moved, the surface will try to superpose itself on all the defining atoms but will not change shape. (To change the shape of the surface, you must select it in the surfmap panel and press Apply).

- The surfaces will follow atoms only while the task (named 'SurfSync') is running.

- A new instance of SurfSync will destroy any previous instances and will reset all surfaces according to the current positions of atoms that define them.

DESCRIPTION:

Prediction of three-dimensional molecular structures using information
from infrared spectra
Hemmer, MC and Gasteiger, J
Analytica Chimica Acta 420 (2000) 145-154

NOTE: Different scaling factors can be found in the literature.
This code does not apply any scaling facor, i.e. both, factor
"f" and "B", as sometimes refered to in the literature,
are both set to one.



This code implements RDF descriptors in a variety of flavours:
1.) Distance:
- topological distances: tRDF*
- geometric distances: gRDF*

2.) Properties encoded:
- none/unweighted: ?RDF*u
- atomic mass: ?RDF*m
- atomic volume: ?RDF*v
- electronegativity: ?RDF*e
- SMR increments: ?RDF*s
- SLogP increments: ?RDF*L
- VSA values: ?RDF*a
- partial charge: Q_?RDF*c
- positive partial charge: Q_?RDF*c++
- negative partial charges: Q_?RDF*c--
(NOTE: partial charges need to be calculated
via DBV|Compute|Molecule|Partial Charge)

- E-Hueckel based strength parameters for hydrogen bond donors
or acceptors: ?RDF*ha and ?RDF*hb



USAGE:

1. Save and load this file
2. The descriptors will be available via DBV|Compute|Descriptors...
3. You can find them by typing "tRDF" or "gRDF" in the
filtering textbox

DESCRIPTION:

Moreau/Broto's Autocorrelation vector descriptors
Moreau, G.; Broto, P. NouV. J. Chim. 1980, 4, 359-360.



This code implements ATS descriptors in a variety of flavours:
1.) Distance:
- topological distances: tATS*
- geometric distances: gATS*

2.) Properties encoded:
- none/unweighted: ?ATS*u
- atomic mass: ?ATS*m
- atomic volume: ?ATS*v
- electronegativity: ?ATS*e
- SMR increments: ?ATS*s
- SLogP increments: ?ATS*L
- VSA values: ?ATS*a
- partial charge: Q_?ATS*c
- positive partial charge: Q_?ATS*c++
- negative partial charges: Q_?ATS*c--
(NOTE: partial charges need to be calculated
via DBV|Compute|Molecule|Partial Charge)

- E-Hueckel based strength parameters for hydrogen bond donors
or acceptors: ?ATS*ha and ?ATS*hb

USAGE:

1. Save and load this file
2. The descriptors will be available via DBV|Compute|Descriptors...
3. You can find them by typing "ATS" in the filtering textbox

Calculates the Sterimol descriptors L, B1, B2, B3, B4, B5.
Load file into MOE. The six new descriptors will appear in the Descriptor panel.
Descriptors are calculated for the whole molecule if no attachment point (A0) is found. If an attachment point is found L is measured along the substitution axis and B1-B4 orthogonal to the substitution axis. (see picture)

MOE-FMOutil consists of several tools and DEMO files.
> Structure preparations (Truncation around the ligand, etc.)
> Input-file preparation (Suport: PIEDA, FMO/PCM, FMO-D optimization, partial MP2 PIE etc.)
> Read and validate GAMESS-FMO inputfile
> Import optimize coodinates into MDB file
> Output Analysis (Summation of Pair Interaction Energy for insisted fragments, Visualize Charge transfer, etc.)

USAGE:
Load fmoutil.svl and type FMOutil []

This script draws cartoons of molecular fragments, to provide non-atomic visualization of macromolecules. The molecular fragments and the corresponding cartoons are read from a customizable dictionary. The current dictionary includes some carbohydrates and nucleic acids.

As described in the accompanying documentation, SEBAS is a program for automatically assigning NMR spectra to a given amino acid sequence. Peak files in standard Sparky and/or NMRPipe format are curated with possible interaction with the user. The curated files are used to construct generic spin systems (GS), and then these are assigned to the given sequence (in FASTA format). Possibly multiple assignments are written out as an Excel spreadsheet for easy examination by the user. Several different assignment algorithms are offered, some being suited to extensive examination of all possible assignments of high quality data, and others are better suited to finding any sort of consensus of assignment from noisier data coming from fewer different triple resonance experiments.

Usage: Simply choose MOE | File | Open then double click on
visualize_dipole.svl

For it to be available for use by all of the users of MOE, create a directory called $MOE/lib/svl/custom/run and save this file to that directory. eg on Windows and Linux, this might be:

c:\moe\lib\svl\custom\run\dbimport.svl
/usr/local/moe/lib/svl/custom/run/dbimport.svl

For the script to be available for use by a single user, then it is best to create a directory in your home directory called svl, and create a directory called run in that directory. Save this file into that directory. eg on Windows and Linux, this might be:

c:\Documents and Settings\user_name\svl\run\dbimport.svl
/usr/people/user_name/svl/run/dbimport.svl

DESCRIPTION:

This plugin computes the change in exposed surface area upon ligand binding to the receptor.

USAGE:

Select the ligand atoms and run this code. The results will be printed into the Commands Window: exposed surface area for a 'free' ligand in its current conformation, and the exposed surface are for the ligand in its complex with the receptor.

Description:
Calculate the ROC curve based on a real activity field and a
predicted activity field. The user has the option of ascending ranks
(small is good) or descending ranks (big is good - default) in both
the real and predicted activity fields. If the used specified
a prediction threshold, a confusion matrix of predicted active/
inactive vs. actual active/inactive is calculated based on the
threshold.

Usage:
(1) Run this file (TED | SVL | Save and Run...)
(2) You will be prompted for a database
(3) The ROC Plot panel will open.
(4) Use the option pull-downs to specify the real and
predicted activity fields. Set the thresholds for each if appropriate.
(5) Press 'Calculate' to perform the matrix calculation.
(6) Use the Radio button at the bottom of the panel to
display the results as either absolute numbers or percentages.
(7) Various statistics are also displayed.
(8) Press the 'Dump Plot to Text' to produce a tab-delimited
file that can be imported in to Excel

ROC "receiver operating characteristic curve" see following links

http:www.statsoft.com/textbook/glosr.html#ROC
http:www.anaesthetist.com/mnm/stats/roc/

Given a database with predicted active and true activity,
the function plots an ROC curve. Sample data from one of the
above links is given at the end of this file.

Usage:
(1) Load this function
(2) Open the database with the activities and predictions.
(3) Enter

svl> ROC_Plot[]

at the SVL command line

(4) Choose the predicted active and the true active fields
(5) Set the thresholds so that compounds where

'Activity Field >= Activity Threshold'

'are deemed "active"' and compounds where

'Prediction Fiels >= Prediction Treshold'

are deemed 'predicted active'

(6) Press 'Plot' to plot the ROC curve.

HITS are CORRECT PREDICTIONS
MISSES are INCORRECT PREDICTIONS

The area beneath the curve is also reported

(7) Press 'Report' to produce a text report including
area calculation and TPF vs. FPF data

Start the program with "Run SVL File". The program will ask for a .kont file. All grids contained in the .kont file will be exported as .grid files in MOE grid format into the directory of the .kont file.
The exported grid files can be visualized with MOE's Surfaces and Maps.

Please note: The binary GRID format conversion is still "experimental". If you encounter problems please contact gkirsten @ chemcomp.com

DESCRIPTION:
A low footprint GUI to set various atom properties on a per chain basis, e.g. visibility, render mode. The interface is based on an original idea by Francis Atkinson and Andrew Leach (GSK).

The application allows you to set the visibility for particular chains (or show only one chain, or show all or no chains), set chain names and tags, select individual or all chains, change the rendering mode from line bonds to stick bonds and alter the potential settings (none, fixed, inert). Which hydrogen atoms are shown can also be changed, and carbon atoms can be coloured by chain or by unique chain tags.

The application will remember if you have hidden atoms in a particular chain, e.g. if you are showing only the pocket atoms from a receptor. If you wish to reset the set of hidden atoms for a particular chain, simply hold down the Shift key and click on the visibility toggle button. If you wish to reset the whole system's hidden status, Shift click the "Show" button at the top.

See the bubble help for the action of each button by holding the mouse pointer over it for a few seconds.

Note:
If there are more than a certain number of chains, the buttons are spread over a set of pages, controlled by < and > buttons in the bottom right of the panel. Currently the maximum number of rows, 18, is hard coded within the script in line 88.

You can make the Chain Manager window stay in the same place when it is recreated after creating or removing chains, set the SAVEPOS constant to 1 in line 89.

USAGE:

1. Save this function
2. Run the SVL, e.g. use the MOE | File | Open panel, browse to chainmanager.svl and click OK.

MOE2005.06 does not support installation on Solaris 10 (due to the fact it was released prior to Solaris 10). The included files will be necessary to install MOE on a machine with Solaris 10, as well as register the license manager as a Solaris 10 service.

Files included in MOE_Solaris.tar:
readme.txt - these instructions
install.sh - MOE install script
arch.sh - MOE architecture redirection script
lmgrd.xml - lmgrd service file

Instructions:
1. Install MOE with the included install.sh file with the -i option. For example, if the install.sh file is at the current directory, and the MOE CD is mounted, type:
./install.sh -i /cdrom/moe_2005_06

2. Replace $MOE/bin/arch.sh with the included arch.sh file.

3. Install the license manager as follows:

a. Edit the paths to your MOE/bin, license file, and log file directories on each of the "exec" lines. In this particular file, MOE is installed at /usr/moe, and the log file resides in the root MOE folder.

b. Copy lmgrd.xml to /var/svc/manifest/application/lmgrd.xml (this is not essential, but this is where all of the other service manifest reside)

c. Type "svccfg import /var/svc/manifest/application/lmgrd.xml"

d. Type "svcadm enable application/lmgrd"

e. The service is now registered and started. It will start on each boot. To stop the license manager, type "svcadm disable application/lmgrd", and to reread the license file, type "svcadm refresh application/lmgrd". To restart the license manager, type "svcadm restart application/lmgrd"

OR
a. Copy the "lmgrd_init" file from the SGI architecture included in MOE to /etc/rc3.d/S95lmgrd_init (replacing the appropriate variables in the script).

b. Treat it as you would any other legacy (pre-Solaris 10) service.


Known Issues:
1. lmgrd is run as root. This can be easily changed by adding "username -c " to the beginning of the commands preceded by "exec" in lmgrd.xml

2. The log file is continuously written, so it may get too large. Simply make a copy of the current log, delete it, and restart the license manager.

This application facilitates exploration of the conformations generated via a conformational search method, including LowModeMD.

It scans a database of conformers and displays the global minimum geometry in the MOE window. Then it display a 2D plot of observed values of 'important'
dihedrals. Next, it allows selection of one or more observed dihedrals and displays the values and some statistics in the MOE window, and selects the
corresponding conformations in the database viewer.

Fingerprint using pharmacophore typing with distances calculated over a reduced molecular graph, after JCIM 2006, 46, 208-220 (DOI: 10.1021/ci050457y)

USAGE:

1. Save and load this function e.g. using MOE | File | Open, browse to ph4erg.svl and click "Load SVL".
2. Calculate fingerprints using DBV | Compute | Fingerprints: the new fingerprint will be available, called FP:ErG. Searches can be performed using the DBV | File | Similarity Search tool.

At the moment the amount of fuzzy incrementation has a default of 0.3. To alter this, set the value of a variable called ERG_FUZZY at the command line, for example:

svl> ERG_FUZZY = 0.2

Fuzzy incrementation as implemented here differs slightly from that described in the paper. The paper suggests that for a particular bit that has e.g. three matching paths in the molecule, that bit and its two neighbours would be:

3*FUZZY, 3, 3*FUZZY

In this implementation that has been changed to:

FUZZY, 1 + FUZZY, FUZZY

In addition each bit can only be incremented once, so if bits A and C were matched in the molecule, bit B would only be incremented once. This has been done in order for the similarity scores to more closely match the ones presented in the paper. If the scheme described in the paper is used, similarity scores are too low due to the large value in the main bit.

The correlation between the scores from this SVL and the paper are still not 100% - I am looking into the reason for this. In my tests with thiscode, the similarity scores are always slightly too low.

The collapse of hydrophobic ring systems into a single Hf feature will occur if any two or more ring systems share more than two atoms.

There is no limit to the number of flip-flop atoms (atoms that are both donor and acceptor). Instead, a descriptor is included in the code (accessible via the MOE tool DBV | Compute | Descriptors, called "flipflop". This allows the user to filter their databases manually prior to fingerprint calculation.

_Normalising the fingerprint_
A database can be used to calculate a normalisation vector, that is a set of numbers by which each fingerprint bit will be multiplied when comparing similarities. This allows the effect of overly-represented bits to be reduced. To create a file of normals, use this command:

svl> CalculateErGNormalisation [mdbfile, normalsfile]

Where both mdbfile and normalsfile are tokens containing the filename of the input MDB file and the output text file. If normalsfile is null then the filename will be {mdbfile}_ergnormals.txt.

A file of normals can be "loaded" into MOE using this command:

svl> LoadErGNormals normalsfile

This will then be used when performing similarity searches.

_Visualisation and Reverse Fingerprinting_

Diagnostic tools are available in this code:

1. If you load a molecule into the main MOE window and run this SVL (e.g. use the MOE | File | Open panel, browse to ph4erg.svl and click OK) then the feature types assigned to the molecule will be drawn in the system. In addition the list of features and the distance matrix of features will be printed in the SVL Commands Window. Only the first feature distance matrix is printed in the case that the molecule contains flipflop atoms. If there are flip flop atoms then for each possibility a different graphic object is created. These can be viewed individually using the Graphics Object Manager.

2. With this SVL file loaded into MOE you can see the scores for each property-distance-property triplet with a molecule loaded in MOE using this command:

svl> Reverse_ErG_Fingerprint ph4_ErG_Fingerprint Atoms[]

Fuzzy incrementation is applied, but weighting is not.

This code is also compatible with the MI reverse fingerprinting package, although as of 10th Sept 2009 it does not elaborate flip flop atoms.

3. ErG Bit Descriptors
This will create a new column for each of the bits in the ErG fingerprint (default settings will create 315 new fields). These are then filled with the counts for each bit. Fuzzy incrementation is applied, but only once, i.e. if a bit is matched three times, that bit and its two neighbours would be:

1*FUZZY, 3, 1*FUZZY

To use this tool, enter the following SVL Command:

svl> ErG_Bit_Descriptors mdb

where mdb is a token containing the filename of an MDB, e.g. 'test.mdb'.

4. ErG Bit Descriptor Heat Map
This will load the descriptors calculated using the previous command from the database and create a graphic object showing the counts as a heat map coloured using a black-body palette.

svl> ErG_Bit_HeatMap mdb

The graphic can be saved in various image formats (JPG, PNG, etc) by clicking the Export button in the Heat Map panel.

Wildman & Crippen, J. Chem. Inf. Comput. Sci., 43, 629-636 (2003).

Given the 3xN array of Cartesian coordinates of N atoms plus N associated "weights" or property values of the atoms, calculate a chirality value for the distribution of the weights in space. The weights must be positive.

This corresponds to CIP chirality when the weights are the atomic weights or priorities of the four substituents of a chiral center, and the coordinates are the coordinates of the substituents. If the four substituents are coplanar or if two weights are the same, the calculated chirality is zero. The mirror image of the configuration gives the opposite sign chirality.

The difference is that this chirality measure is a continuous function of the coordinates and the weights of four or more points. Not only does it give the same value when the coordinates are transformed by a translation or proper rotation, but the value is unchanged when the coordinates or the weights are multiplied by a positive constant.

SMARTCyp predicts positions in drug-like molecules that are likely sites for metabolism by cytochromes P450. It acts as a wrapper for the SMARTCyp method within MOE.

SMARTCypMOE processes single molecules loaded in the main MOE window or groups of molecules stored in a MOE molecular database. Molecular databases can be processed interactively from MOE’s Database Viewer or in batch mode via the moebatch program. Reactivity ranks and scores are added to the molecular database and the results can be viewed graphically in the MOE Database Browser.

Using a heme model system,Rydberg et al. computed transition state energies for more than 200 fragments and from these energies and one accessibility-like descriptor a very simple scoring function was deduced which is used to score and rank atoms in a molecule. The model covers all common reactions in P450s and has a good performance for all P450 isoforms except 2C9 and 2D6.

Score = Reactivity – 8 x Accessbility

Where Reactivity is the activation energy in kJ/mol and the accessbility is the span descriptor which measures the relative topological distance to the end of the molecule (which always is between 0.5 and 1).

Prerequisites:
The SMARTCyp executable jar file which can be downloaded at http://www.farma.ku.dk/smartcyp/

The distribution is a zip archive containing the SVL script (cypscore.svl), entries for the moe-menus file (moe-menus-cypscore) and a manual (SMARTCypMOE.pdf), which provides full details on how to install and use the script.

References:
Patrik Rydberg, David E. Gloriam, Jed Zaretzki, Curt Breneman and Lars Olsen (2010) "SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism" ACS Med. Chem. Lett., 1, 96-100
Patrik Rydberg, David Gloriam and Lars Olsen (2010) "The SMARTCyp cytochrome P450 metabolism prediction server" Bioinformatics, DOI: 10.1093/bioinformat

Divides numerical data into bins defined by the lower bounds, the upper bonds
and width. Optionally, bins are divided by the number of bins if the width is
not set. The values normalized to the lower bounds of bins will be written in
the input MDB and the lower bounds and the frequencies of bins will be output
to the other MDB.

The histogram of the values of fieldname and their lower bounds
within their bins in Gaussian distribution will be plotted.

Installation:

(a) Place dbv_histogram.svl in $MOE/lib/svl/custom/ directory
and invoke MOE for the general use
(b) Place dbv_histogram.svl in $HOME/svl/ directory
and invoke MOE for the personal use
(c) From the MOE | File | Open panel, select this file
and click the "Load" button for the temporal use.

USAGE:

moe> db_Histogram [filename: '/home/userid/work/filename.mdb', \
nfname: 'SlogP', \
esel: 0, lb: -9, ub: 9, w: 0.5]
('\' stands for the continuation, i.e., enter these in a single line)

or

1. Select a field in MDB on DBV,
2. DBV | CLI | dbv_Histogram [lb: -9, ub: 9, w: 0.5]

where the parameters are:

filename: the mdb filename
If not specified, the filename of the default view will
be used.
nfname: the numeric field name to calculate
The default is the first numerical field name.
esel: calculate only on the selected entries
lb: the value of the lower bound
The default is the minimum value in the field.
ub: the value of the upper bound
The default is the maximum value in the field.
w: the width of the bins
If not specified, (ub - lb) / nbin will be used.
nbin: the number of bins
The default value is 10.
If w is specified, (ub - lb) / w will be used.
openodb: If on, open the output database in database viewer
The default is 1 (on) for GUI
and 0 (off) for batch mode.
drawhis: If on, draw the histogram
The default is 1 (on) for GUI
and 0 (off) for batch mode.

e.g.,

DBV | CLI | dbv_Histogram [nfname: 'SlogP', lb: -9, ub: 9, w: 0.5]

or

1. Select a field,
2. DBV | CLI | dbv_Histogram []
3. Set optional parameters and click OK

if w is given, nbin will be ignored.

The outputs are:
filename.mdb:
l_fieldname: the lower bound of the descriptor in each bin

filename_fieldname_histogram.mdb
l_fieldname: the lower bound of the descriptor in each bin
f_fieldname: the frequencies of the entry in each bin

The histogram between the values of fieldname and their lower bounds
within their bins in Gaussian distribution will be plotted.

There are three main routines you can call :

gamin 'gamess.log'
Update co-ordinates and charges of current Moe molecule with those from the gamess output file. You MUST have the appropriate molecule already read into Moe or the results will be unpredictable.

gampotgui 'gamess.log'
Read the molecular electrostatic potential (MEP) from the gamess output. Contour the MEP on a grid around the molecule using a Gui to control the three contour levels. Works with the 2010 version of Gamess (WinGamess 10).

gamess_input_gui []
Produces a Gui to generate basic Gamess input files. input is a 6-31G** Hartree Fock calculation. You will need to change the basis set manually but you can select an option in the Gui to perform a B3-LYP calculation. The input file is generated when you enter a file name and press return. A .moe file of the current structure is saved at the same time. This is needed when you come to analyse the results.

There is an option to select a Z-matrix (i.e. internal co-ordinate) input file / optimisation rather than the cartesian input normally produced by Moe. This can lead to faster optimisation - I achieved optimisation of 3-Me pyridine in 7 cycles rather than 42 with cartesian, so quite a useful saving.

It is possible to select a partial Z-matrix optimisation in which some degrees of freedom (DoFs) are frozen at their input values. This is handy for calculating strain energies, where you can freeze all torsion angles and allow the bonds and angles to relax. A Gui is generated which allows interactive selection of the frozen DoFs.

The input file for the MEP calculation can also be generated. This option generates a grid of points around the molecule which can be manipulated using a Gui to give the size of grid you need to observe features of interest.

Note : optimisation will move the molecule slightly from the starting point, so you may want to optimise first and then do a second calculation without opt. to calculate your MEP.
Z-matrix optimisation moves the atoms to the co-ordinate origin, so your grid WILL be in the wrong place.

The default version of Gamess limits you to only 100 grid points. You will need to recompile it. Go to the source directory in the unpacked distribution and edit prplib.src. Global replace MXPTPT=100 with MAXPTPT=99999. Then save and follow the compilation instructions. (Easier on linux than windows as you will need compilers, which only come free with good operating systems).

How to use the script:

In MOE command window:
load 'selectNentry.svl'

Individual functions can be used as follows:

selectNtry [num_entry, which_entry, entry_option]
examples:
selectNtry [20,0,0] for selecting every 20th entry of the database
selectNtry [20,1,0] for selecting every 21st entry of the database
selectNtry [20,2,0] for selecting every 22nd entry of the database
selectNtry [5,0,1] for selecting every 5th entry and creating a new group for it


indexgroup [fieldname, indexname]
Where:
fieldname: name of the field that needs to be indexed
indexname: name of the index field that will be created
example: indexgroup ['mseq','Rank']

selectdups [fieldname]
Where:
fieldname: name of the field in which duplicates will be selected
example: selectdups ['CMPDID']
Note: The field should be sorted before running the command

replacentry [fieldname, searchstrng, replacestrng, esel]
Where:
fieldname: name of the field in which the string to be replaced will be searched for.
searchstrng: this string will be searched in the fieldname of the database
replacestrng: this string will replace the search string in all entries of the database
esel: if set to 1 will operate on selected entries otherwise entire database
example: findentry ['HIT_TYPE',"HTS1","HTS2",1]
Note: string for searching and replacement should be in double quotes "

DESCRIPTION
This utility allows import/exporting MOE-Restraints from/to tab-separated-text files. It also compares the existing MOE-restraints vs. the currently loaded
structures and saves the 'unsatisfied' restraints to a text file.

This function allows BREED operations on aligned structures with general formula of A-B-C where B is a ring system. For n input structures, it generates all A(n)-B(n)-C(n) combinations. A demo input file is provided (_rb_demo.moe).

The -protomer switch in sdwash creates the tautomers along with protomers.In case enumerating new tautomers from the input structures is undesirable this program can be used to mark the protomers in the enumerated database, so that they can be separated from the tautomers.

This is a set of scripts that allow the CEPOS InSilico Ltd. programs ParaSurf and ParaFit to be run from within MOE. ParaSurf generates molecular surface properties based on semi-empirical molecular orbital calculations. ParaFit superposes and compares molecules using the spherical harmonic expansions of the molecular surface and properties calculated by ParaSurf. The scripts enable the user to run ParaSurf on single molecules or molecular databases; use ParaSurf descriptors and rotationally invariant fingerprints (RIFs) in MOE QuaSAR; visualize ParaSurf surfaces and properties; align molecules with ParaFit based on their ParaSurf properties; and produce ParaFit 'canonical' alignments.

Prerequisites:
ParaSurf and ParaFit are available from CEPOS InSilico Ltd. (www.ceposinsilico.com). ParaSurf requires the results of semi-empirical molecular orbital calculations, which can be provided either by VAMP or by a public domain version of MOPAC that is available for free download from www.ceposinsilico.com.

The distribution is a zip archive containing the SVL scripts, entries for the moe-menus file and a manual in PDF format that provides full details on how to install and use the scripts.

Radial Distribution Function Descriptors
based upon:
Hemmer, M. C.; Steinhauer, V.; Gasteiger, J.
J. Vibrat. Spect., (1999), 19, 151-164
The Coefficients f and B have been derived from fits against literature data.

This is an independent implementation (D.Bondarev) of the reference below:

J.Chem.Inf.Comput.Sci. 43(4), pp. 1151-1157, 2003
Design and Evaluation of a Molecular Fingerprint Involving the Transformation of Property Descriptor Values into a Binary Classification Scheme. Ling Xue, Jeffrey Godden, Florence Stahura, Jurgen Bajorath.

USAGE:
Load this file with the MOE File Open panel (click OK).

You can then use MOE fingerprint tools as usual.

Compute the MPMFP fingerprint or use any fingerprint tools (including any scripts from this website). The fingerprint code is MPMFP, the similarity metric code is avg-tanimoto.

This is an implementation of the CypScore method devised by Hennemann et al. for the prediction of cytochrome P450 reactive sites in drug-like molecules. The script processes single molecules loaded in the main MOE window or groups of molecules stored in a molecular database. Molecular databases can be processed interactively from MOE’s Database Viewer or in batch mode via the moebatch program. Reactivity scores are added to the molecular database and the results can be viewed graphically in the MOE Database Browser.

For six oxidation reactions, Hennemann et al. derived linear models predicting an atom’s reactivity from the values of descriptors calculated by ParaSurf, with each model producing reactivity predictions on an individual scale. The original paper described a procedure to establish a common reactivity scale for all the models, providing a CypScore value for each atom in the range from 0 (stable) to 100 (reactive), and implemented this procedure for a proprietary data set. Unfortunately, Hennemann et al. do not include sufficient detail to define completely the relationship between the original model values and the scaled scores, and it is not possible to reproduce the missing information as the data from which it was derived is not available publicly. To overcome this, this script employs a common reactivity scale that is a close approximation to the one used by Hennemann et al., derived from secondary information in the original paper through a process described in the accompanying manual. Consequently, the results obtained will approximate closely, but not match exactly, those in the original publication.

Prerequisites:
The CypScore predictions are based on molecular surface descriptors calculated by ParaSurf, which is available from CEPOS Insilico Ltd. (www.ceposinsilico.com). ParaSurf requires the results of semi-empirical molecular orbital calculations, which can be provided either by VAMP or by a public domain version of MOPAC that is available for free download from www.ceposinsilico.com.

The distribution is a zip archive containing the SVL script (cypscore.svl), entries for the moe-menus file (moe-menus-cypscore) and a manual (CypScoreMOE.pdf), which provides full details on how to install and use the script.

Reference:
M. Hennemann, A. Friedl, M. Lobell, J. Keldenich, A. Hillisch, T. Clark and A. H. Goller. CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory. ChemMedChem, 2009, 4

Usage:
(1) Load this function
(2) Enter

svl> GA[]

at the SVL command line

You can get linear model, perform cross validation and
save your models in mdb file format from this panel.

To evaluate new molecules with your model, enter

svl> ModelCalc[]

-----------
TUTORIAL:

'$MOE/sample/mol/blood_brain.mdb' may be used as a tutorial.

(1)Calculate 32 VSA descriptors and TPSA
(2)Run QuaSAR-Evolution with default parameters
Equation file 'ga_*_eq.mdb' and trajectory file 'ga_*_trj.mdb' will open
(3)Plot SMR_VSA4 and SlogP_VSA0 in plot area of trajectory file.
and observe how equations evolve
(4)Calculate TPSA descriptor
(5)Click 'CONTINUE'
(6)Plot TPSA and observe equations
You will see that the combination of SMR_VSA4
and TPSA is very important for good model